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 |
|---|---|---|---|---|---|---|---|---|---|---|
GLU | # 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... | venisehannoyer/Hear-me-GirlsInAI-team1 | GLU | false | 10,978 | [
"Apache-2.0"
] | 0 | 664b3af4befe9b73c28d4362969699bc2254bdf9 | https://github.com/venisehannoyer/Hear-me-GirlsInAI-team1/tree/664b3af4befe9b73c28d4362969699bc2254bdf9 |
DAImgHead | import torch
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.cuda.amp
class DAImgHead(nn.Module):
"""
Add a simple Image-level Domain Classifier head
"""
def __init__(self, in_channels):
"""
Arguments:
in_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | enpko47/DA-CenterNet | DAImgHead | false | 3,708 | [
"MIT"
] | 0 | ef0a99b8ba741fa1dbd66fa58ccae9bf8759ae86 | https://github.com/enpko47/DA-CenterNet/tree/ef0a99b8ba741fa1dbd66fa58ccae9bf8759ae86 |
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.triton_helpers import libdevice
import math
from to... | iamstevepaul/MRTA-Attention | Attention | false | 10,230 | [
"MIT"
] | 0 | fc177440f7354212c41ad02ef76fdda43cc0aa57 | https://github.com/iamstevepaul/MRTA-Attention/tree/fc177440f7354212c41ad02ef76fdda43cc0aa57 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as f
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv = nn.Conv2d(1, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc = nn.Linear(2304, 10)
def forward(self, x):
x = self.poo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | aabobakr/adversarial-robustness-toolbox | Model | false | 9,653 | [
"MIT"
] | 0 | d62b2606132d6e6fd5946d6bdc8f1da940eb3282 | https://github.com/aabobakr/adversarial-robustness-toolbox/tree/d62b2606132d6e6fd5946d6bdc8f1da940eb3282 |
Pooler | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
return f
class DropoutWrapper(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_schedu... | chunhuililili/mt_dnn | Pooler | false | 10,205 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
CRF | import torch
import torch.nn as nn
import torch.nn.init
import torch.nn.functional as F
class CRF(nn.Module):
"""
Conditional Random Field Module
Parameters
----------
hidden_dim : ``int``, required.
the dimension of the input features.
tagset_size : ``int``, required.
the siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo... | INK-USC/ConNet | CRF | false | 8,271 | [
"MIT"
] | 11 | adb299f160556004561df302c19578200bd3835b | https://github.com/INK-USC/ConNet/tree/adb299f160556004561df302c19578200bd3835b |
posFFN1d | import torch
from torch import nn
class posFFN1d(nn.Module):
def __init__(self, d_hid, d_inner_hid, window=1, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_hid, d_inner_hid, kernel_size=window)
self.relu = nn.ReLU()
self.w_2 = nn.Conv1d(d_inner_hid, d_hid, kernel_size=wi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | chaitanya2334/lsm | posFFN1d | false | 1,673 | [
"MIT"
] | 0 | 504c732238b419cd77e7e0a97af040778ee9c7dd | https://github.com/chaitanya2334/lsm/tree/504c732238b419cd77e7e0a97af040778ee9c7dd |
Model | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, action_dim, conv=False, conv_size=16,
fc_size=32, K=2):
super(Model, self).__init__()
self.input_size = input_size
self.input_h = int(np.sqr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | VashishtMadhavan/pytorch-maml-rl | Model | false | 1,174 | [
"MIT"
] | 0 | d8821b8374d973869bb6a1393f1b2c369c9a664b | https://github.com/VashishtMadhavan/pytorch-maml-rl/tree/d8821b8374d973869bb6a1393f1b2c369c9a664b |
MLP | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/SuPar | MLP | false | 11,639 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
Model | # 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.nn import Module
import torch.nn.functional
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn... | Liuhongzhi2018/Person_ReID | Model | false | 4,754 | [
"MIT"
] | 0 | 51c576ed5b4ed960801669d6d59c0a77405b369d | https://github.com/Liuhongzhi2018/Person_ReID/tree/51c576ed5b4ed960801669d6d59c0a77405b369d |
PinballLoss | import torch
import torch.nn as nn
class PinballLoss(nn.Module):
"""Computes the pinball loss between y and y_hat.
y: actual values in torch tensor.
y_hat: predicted values in torch tensor.
tau: a float between 0 and 1 the slope of the pinball loss. In the context
of quantile regression, the value of alph... | 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... | cchallu/esrnn | PinballLoss | false | 6,415 | [
"MIT"
] | 1 | 543ca365c70be2775a4b5863820b246071ccde3c | https://github.com/cchallu/esrnn/tree/543ca365c70be2775a4b5863820b246071ccde3c |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
import torch.distributed
assert_size_str... | ESCM-summarization/ESCM-summary-evaluation | ContextGate | false | 9,121 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 |
ContrastiveEmbeddingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | bbradt/catalyst | ContrastiveEmbeddingLoss | false | 3,183 | [
"Apache-2.0"
] | 0 | 38a503c8af040906e377b7485d7fe15a7bc1de19 | https://github.com/bbradt/catalyst/tree/38a503c8af040906e377b7485d7fe15a7bc1de19 |
MinibatchStdLayer | import torch
import torch.nn as nn
class MinibatchStdLayer(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, group_size=4):
group_size = min(group_size, x.shape[0])
_channels, height, width = x.shape[1:]
y = x.view(group_size, -1, *x.shape[1:])
y... | 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_... | DannyDannyDanny/DeepPrivacy | MinibatchStdLayer | false | 2,123 | [
"MIT"
] | 0 | 749e260bdcc28a0c12d526f24e4f5315d1b447ad | https://github.com/DannyDannyDanny/DeepPrivacy/tree/749e260bdcc28a0c12d526f24e4f5315d1b447ad |
SelfAttention | import torch
from torch.nn import functional as F
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, k, heads=8):
super().__init__()
self.k, self.heads = k, heads
self.toKeys = nn.Linear(k, k * heads, bias=False)
self.toQueries = nn.Linear(k, k * heads, bias=F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jiyfeng/transformer-text-tasks | SelfAttention | false | 3,757 | [
"MIT"
] | 0 | b06349ca759bc8084a5880a425153dfdd7b91a98 | https://github.com/jiyfeng/transformer-text-tasks/tree/b06349ca759bc8084a5880a425153dfdd7b91a98 |
ExponentialUpdate | import torch
from torch import Tensor
from torch import nn
from torch.jit import Final
class ExponentialUpdate(nn.Module):
alpha: 'Final[int]'
def __init__(self, alpha: 'float'):
super().__init__()
self.alpha = float(alpha)
def forward(self, x: 'Tensor', state: 'Tensor') ->Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.jit import Final
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | Rikorose/clc-dns-challenge-2020 | ExponentialUpdate | false | 8,694 | [
"Apache-2.0"
] | 12 | 4f1c078691327a75b3a338fe372ba356b450a6da | https://github.com/Rikorose/clc-dns-challenge-2020/tree/4f1c078691327a75b3a338fe372ba356b450a6da |
UnbalancedLoss | import torch
import torch.nn as nn
import torch.utils.data
class UnbalancedLoss(nn.Module):
NUM_LABELS = 2
def __init__(self):
super().__init__()
self.crit = nn.BCEWithLogitsLoss()
def forward(self, logits, label):
return self.crit(logits, label)
def get_inputs():
return [t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Kausta/DeepGlobalRegistration | UnbalancedLoss | false | 9,180 | [
"MIT"
] | 0 | 4f087d4c775f607e335616e95d8fb28e53d4b823 | https://github.com/Kausta/DeepGlobalRegistration/tree/4f087d4c775f607e335616e95d8fb28e53d4b823 |
Block | import torch
import torch.nn as nn
import torch.autograd
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CASIA-IVA-Lab/PASS_reID | Block | false | 17,053 | [
"Apache-2.0"
] | 5 | 46dc6d25f4396e35ac1a766ad2dcaa580beccf15 | https://github.com/CASIA-IVA-Lab/PASS_reID/tree/46dc6d25f4396e35ac1a766ad2dcaa580beccf15 |
BilinearMap | # 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 as th
import torch.nn as nn
from torch.nn.parameter import Paramete... | IBM/aihn-ucsd | BilinearMap | false | 8,263 | [
"Apache-2.0"
] | 20 | 6c6a56d11c704b529a31868418e350e9760ff9d9 | https://github.com/IBM/aihn-ucsd/tree/6c6a56d11c704b529a31868418e350e9760ff9d9 |
pair_norm | import torch
class pair_norm(torch.nn.Module):
def __init__(self):
super(pair_norm, self).__init__()
def forward(self, x):
col_mean = x.mean(dim=0)
x = x - col_mean
rownorm_mean = (1e-06 + x.pow(2).sum(dim=1).mean()).sqrt()
x = x / rownorm_mean
return x
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ngohienduong/Deep_GCN_Benchmarking | pair_norm | false | 16,184 | [
"MIT"
] | 70 | 3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 | https://github.com/ngohienduong/Deep_GCN_Benchmarking/tree/3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 |
LIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(LIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = Parameter(torch.Tensor(1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | ldzhangyu/photo2cartoon | LIN | false | 3,874 | [
"MIT"
] | 0 | d5b371e77e61018c28109db67e8306e5e6064800 | https://github.com/ldzhangyu/photo2cartoon/tree/d5b371e77e61018c28109db67e8306e5e6064800 |
NetVLAD | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=16, dim=2048, alpha=30.0,
normalize_input=True):
"""
Args:
num_cluster... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fede-vaccaro/cnnimageretrieval-pytorch | NetVLAD | false | 12,656 | [
"MIT"
] | 0 | 56bf4ee865e9769801819943f75fff207f0c2f00 | https://github.com/fede-vaccaro/cnnimageretrieval-pytorch/tree/56bf4ee865e9769801819943f75fff207f0c2f00 |
AsymmetricLoss | # 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... | CAMP-eXplain-AI/imba-explain | AsymmetricLoss | false | 2,060 | [
"MIT"
] | 0 | e41b4ca5de63955cb0e925aad9599f38c5a3e973 | https://github.com/CAMP-eXplain-AI/imba-explain/tree/e41b4ca5de63955cb0e925aad9599f38c5a3e973 |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | activeloopai/gpt-neox | GEGLU | false | 6,068 | [
"MIT"
] | 1 | 89749e0b76938fa1ff84a3dd1cbcbe64521d861b | https://github.com/activeloopai/gpt-neox/tree/89749e0b76938fa1ff84a3dd1cbcbe64521d861b |
TAGConv | import torch
import torch.nn as nn
class TAGConv(nn.Module):
def __init__(self, in_features, out_features, K=3, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.w = nn.Linear(in_features * (self.K + 1), out_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/GraphGallery | TAGConv | false | 13,646 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
RC | # 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.... | benningtonlee7/AdaIn_Style_Transfer_From_Scratch_In_Pytorch | RC | false | 6,332 | [
"MIT"
] | 1 | 50dfe4bdcbcdd0f4e647f9ee45de2a3f81eb6722 | https://github.com/benningtonlee7/AdaIn_Style_Transfer_From_Scratch_In_Pytorch/tree/50dfe4bdcbcdd0f4e647f9ee45de2a3f81eb6722 |
reg_pos | import torch
import torch.nn as nn
class reg_pos(nn.Module):
def __init__(self):
super(reg_pos, self).__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, h_pred, h_label):
l1_loss = h_label[:, 1, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
(h_lab... | 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
... | FrancesC0de/Pedestron | reg_pos | false | 9,102 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca |
BinaryReg | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
Args:
pred (torch.Tensor): foreground logits.
mask (Optional[torch.Tensor], optional): weight... | 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... | Atharva-Peshkar/pytorch_connectomics | BinaryReg | false | 13,305 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
Transformer | import torch
class Convlayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
super().__init__()
padding = kernel_size // 2
self.refl = torch.nn.ReflectionPad2d(padding)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bruchano/ImageStyler | Transformer | false | 9,945 | [
"MIT"
] | 0 | 7bde13bc954566088c477065adb5c4e4214c28bb | https://github.com/bruchano/ImageStyler/tree/7bde13bc954566088c477065adb5c4e4214c28bb |
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.... | LuckierDodge/ROS_Dockerfiles | Actor | false | 2,613 | [
"MIT"
] | 0 | 42fd0e7ecfef86d792fcc29197fcd79dcb789122 | https://github.com/LuckierDodge/ROS_Dockerfiles/tree/42fd0e7ecfef86d792fcc29197fcd79dcb789122 |
UNetSeeInDark | # 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... | Vandermode/ELD | UNetSeeInDark | false | 14,603 | [
"MIT"
] | 258 | aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 | https://github.com/Vandermode/ELD/tree/aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 |
Follow_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.autograd import Variable
assert_size_stride = torch._C._dynamo.guards.assert_s... | B06901052/deep-stabilization | Follow_loss | false | 104 | [
"Apache-2.0"
] | 0 | b6030b463cf1f1128660e900669f43e742aa2651 | https://github.com/B06901052/deep-stabilization/tree/b6030b463cf1f1128660e900669f43e742aa2651 |
BellMembFunc | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | GradyKurpasi/anfis-pytorch | BellMembFunc | false | 9,084 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 |
SAM | import torch
from torch import nn
class SAM(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels=1, kernel_size=1)
def forward(self, x):
spatial_features = self.conv(x)
attention = torch.sigmoid(spatial_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | SAM | false | 15,078 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
TemperatureHolder | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | g-votte/pfrl | TemperatureHolder | false | 15,393 | [
"MIT"
] | 824 | 4c30c1d73f0941a2b649b62937eec346bb55a95e | https://github.com/g-votte/pfrl/tree/4c30c1d73f0941a2b649b62937eec346bb55a95e |
CrossEntropyWithLogSoftmax | # 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... | cadurosar/graph_kd_dense_cifar100 | CrossEntropyWithLogSoftmax | false | 1,620 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
D_UpBlock | import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | D_UpBlock | false | 9,557 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
CNNLayerNorm | import torch
import torch.nn as nn
class CNNLayerNorm(nn.Module):
"""Layer normalization built for cnns input"""
def __init__(self, n_feats):
super(CNNLayerNorm, self).__init__()
self.layer_norm = nn.LayerNorm(n_feats)
def forward(self, x):
x = x.transpose(2, 3).contiguous()
... | 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_... | HikaruHotta/M2M-VC-CycleGAN | CNNLayerNorm | false | 17,368 | [
"MIT"
] | 5 | a93b06221c787cc3e13b2d92fee728b811e5d526 | https://github.com/HikaruHotta/M2M-VC-CycleGAN/tree/a93b06221c787cc3e13b2d92fee728b811e5d526 |
MV_Softmax | from torch.nn import Module
import math
import torch
from torch.nn import Parameter
import torch.nn.functional as F
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class MV_Softmax(Module):
"""Implementation for "Mis-classified Vector Guided Softmax Loss for Face R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Capetian/FaceX-Zoo | MV_Softmax | false | 4,975 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
Encoder | # 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 ... | MaurizioFD/recsys-challenge-2020-twitter | Encoder | false | 8,518 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
VAE | # 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 import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | Jack12xl/scene-representation-networks | VAE | false | 606 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
SetConv | import torch
from torch.nn import functional as F
import torch.nn as nn
class SetConv(nn.Module):
def __init__(self, sample_feats, predicate_feats, join_feats, hid_units):
super(SetConv, self).__init__()
self.sample_mlp1 = nn.Linear(sample_feats, hid_units)
self.sample_mlp2 = nn.Linear(hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | danield137/deep_query_optimzation | SetConv | false | 1,790 | [
"MIT"
] | 0 | 01a25c966338007f15d14dea1b37e388e47bcfe3 | https://github.com/danield137/deep_query_optimzation/tree/01a25c966338007f15d14dea1b37e388e47bcfe3 |
ConvHardtanh | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvHardtanh(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvHard... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | XiaobingSuper/intel-extension-for-pytorch | ConvHardtanh | false | 9,721 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
SplitCosineLinear | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class CosineLinear(Module):
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mhd-medfa/class-incremental-learning | SplitCosineLinear | false | 16,039 | [
"MIT"
] | 241 | c7c0a217d07b285f215672b3021beee52d4ef74f | https://github.com/mhd-medfa/class-incremental-learning/tree/c7c0a217d07b285f215672b3021beee52d4ef74f |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
return 1 - (2 * torch.sum(output * target) + self.smooth) / (torch.
... | 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... | LisburnLad/open-solution-salt-detection | DiceLoss | false | 777 | [
"MIT"
] | 0 | 9ac292700b2f1351244e29e039425ee706aab92a | https://github.com/LisburnLad/open-solution-salt-detection/tree/9ac292700b2f1351244e29e039425ee706aab92a |
QNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | KtechB/machina | QNet | false | 2,472 | [
"MIT"
] | 0 | 24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab | https://github.com/KtechB/machina/tree/24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab |
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.... | Malkovsky/NeMo | MultiHeadAttention | false | 2,627 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
InteractingLayer | # 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.... | zzz123xyz/DeepCTR-Torch | InteractingLayer | false | 4,750 | [
"Apache-2.0"
] | 0 | d6b880cc6b3761dbef90920a28182ef6737dd665 | https://github.com/zzz123xyz/DeepCTR-Torch/tree/d6b880cc6b3761dbef90920a28182ef6737dd665 |
Net | # 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_... | DataCanvasIO/YLearn | Net | false | 17,215 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a |
Policy | # 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.... | jiruifu-jerry0219/DRLND_Jerry | Policy | false | 3,746 | [
"MIT"
] | 0 | 6a342f99119d466f8ae96202452b034f1a2e70e1 | https://github.com/jiruifu-jerry0219/DRLND_Jerry/tree/6a342f99119d466f8ae96202452b034f1a2e70e1 |
GlobalAveragePooling | # 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... | xh-liu-tech/CIPS-3D | GlobalAveragePooling | false | 11,106 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
ConvBlock | import torch
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | grofit/traiNNer | ConvBlock | false | 15,465 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
QRNNLayer | import torch
import torch.nn as nn
from torch.optim import *
class ForgetMult(torch.nn.Module):
"""ForgetMult computes a simple recurrent equation:
h_t = f_t * x_t + (1 - f_t) * h_{t-1}
This equation is equivalent to dynamic weighted averaging.
Inputs: X, hidden
- X (seq_len, batch, input_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.triton_helpers import libdevice
import torch.nn as ... | MochizukiShinichi/NeuronBlocks | QRNNLayer | false | 9,348 | [
"MIT"
] | 0 | ee15beb564b35900a179fe767745d031124273e9 | https://github.com/MochizukiShinichi/NeuronBlocks/tree/ee15beb564b35900a179fe767745d031124273e9 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sreekanth-sreekumar/daiz-woz-nlp-project | TransformerEncoderLayer | false | 4,390 | [
"MIT"
] | 0 | 9971f752aee6a850e265f15e97a3a1ef2dacd323 | https://github.com/sreekanth-sreekumar/daiz-woz-nlp-project/tree/9971f752aee6a850e265f15e97a3a1ef2dacd323 |
LogSTFTMagnitudeLoss | # 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... | ChanganVR/hifigan-denoiser | LogSTFTMagnitudeLoss | false | 13,455 | [
"Apache-2.0"
] | 100 | 9bd77c53556e1372b4bbff8dce8b120297cc4e5c | https://github.com/ChanganVR/hifigan-denoiser/tree/9bd77c53556e1372b4bbff8dce8b120297cc4e5c |
LayerNormChannel | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | hyenal/tensorflow-image-models | LayerNormChannel | false | 3,637 | [
"Apache-2.0"
] | 0 | 2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c | https://github.com/hyenal/tensorflow-image-models/tree/2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c |
NST | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class NST(nn.Module):
"""
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
https://arxiv.org/pdf/1707.01219.pdf
"""
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
import... | Capetian/FaceX-Zoo | NST | false | 4,970 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
BasicDeconv | # 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_... | DecAngel/C-3-Framework | BasicDeconv | false | 363 | [
"MIT"
] | 0 | 00b792b4481a0ec9d7e30e290c66e7020235e79b | https://github.com/DecAngel/C-3-Framework/tree/00b792b4481a0ec9d7e30e290c66e7020235e79b |
upsample_block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Art-MC/SKX_NN | upsample_block | false | 8,844 | [
"MIT"
] | 0 | 02d5089ea9c4b3ca7c1878e1d9a5811f5da9f6bd | https://github.com/Art-MC/SKX_NN/tree/02d5089ea9c4b3ca7c1878e1d9a5811f5da9f6bd |
UnpackLayerConv2d | import torch
import torch.nn as nn
class Conv2D(nn.Module):
"""
2D convolution with GroupNorm and ELU
Parameters
----------
in_channels : int
Number of input channels
out_channels : int
Number of output channels
kernel_size : int
Kernel size
stride : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | aycatakmaz/packnet-sfm | UnpackLayerConv2d | false | 12,144 | [
"MIT"
] | 0 | d89cae81290133f136f6a1d1e288affc67eed1f7 | https://github.com/aycatakmaz/packnet-sfm/tree/d89cae81290133f136f6a1d1e288affc67eed1f7 |
UpSampleX2 | # 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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | lizatish/My_CNN | UpSampleX2 | false | 10,426 | [
"MIT"
] | 0 | b13818bcce2f8a3697d20e34157e3dce53f953ee | https://github.com/lizatish/My_CNN/tree/b13818bcce2f8a3697d20e34157e3dce53f953ee |
Highway | import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.functional
class Highway(nn.Module):
"""The Highway update layer from [srivastava2015]_.
.. [srivastava2015] Srivastava, R. K., *et al.* (2015).
`Highway Networks <http://arxiv.org/abs/1505.00387>`_.
*arXiv*,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | YuWVandy/chemicalx | Highway | false | 1,276 | [
"Apache-2.0"
] | 0 | c02f979a502409c26700e6d5a1b2e6c0aa77e64c | https://github.com/YuWVandy/chemicalx/tree/c02f979a502409c26700e6d5a1b2e6c0aa77e64c |
L2Conv2D | # 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.... | M-Nauta/ProtoTree | L2Conv2D | false | 8,517 | [
"MIT"
] | 35 | 72cad5e42b0eb05c1312e5496f36b842726e081a | https://github.com/M-Nauta/ProtoTree/tree/72cad5e42b0eb05c1312e5496f36b842726e081a |
Mask | import torch
import torch.nn as nn
class Mask(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
return seq.where(torch.eq(seq_mask, 1), torch.zeros_like(seq))
def get_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | charliemorning/mlws | Mask | false | 1,669 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 |
MCCRLoss | # 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... | appleparan/mise.py | MCCRLoss | false | 6,219 | [
"MIT"
] | 1 | a77ea51be37a739928600c66d168d69b78bc0c4b | https://github.com/appleparan/mise.py/tree/a77ea51be37a739928600c66d168d69b78bc0c4b |
L1Loss | # 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 functools
impor... | JasonBoy1/mmhuman3d | L1Loss | false | 5,378 | [
"Apache-2.0"
] | 1 | 79b2665191115f3ed905e6afdf09990a8d484362 | https://github.com/JasonBoy1/mmhuman3d/tree/79b2665191115f3ed905e6afdf09990a8d484362 |
A | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ModECI/MDF | A | false | 8,553 | [
"Apache-2.0"
] | 12 | 76d5db6a1c9f691ca5be36d60d28e6e529762e7e | https://github.com/ModECI/MDF/tree/76d5db6a1c9f691ca5be36d60d28e6e529762e7e |
FocalLoss | # 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... | Lascarfo/kaggle-landmark-recognition-2020-1st-place | FocalLoss | false | 2,501 | [
"MIT"
] | 0 | f9007d81e59ecd1311bdea5586a426b8973a2eb8 | https://github.com/Lascarfo/kaggle-landmark-recognition-2020-1st-place/tree/f9007d81e59ecd1311bdea5586a426b8973a2eb8 |
ClassBlock | import torch
import torch.nn as nn
import torch.nn.parallel
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | ClassBlock | false | 11,520 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
Conv2 | import torch
from torch import nn
from torch.nn import Conv2d
from torch.nn import Conv3d
class Conv2(nn.Module):
def __init__(self):
super(Conv2, self).__init__()
self.conv1 = Conv2d(in_channels=10, out_channels=2, kernel_size=5,
padding=2, bias=True)
self.conv2 = Conv3d(in_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import Conv2d
from torch.nn import Conv3d
ass... | pvgladkov/abstraction-and-reasoning-challenge | Conv2 | false | 12,912 | [
"MIT"
] | 0 | 0dfe16b5044f5aba0d5f53397dc615400e61aa69 | https://github.com/pvgladkov/abstraction-and-reasoning-challenge/tree/0dfe16b5044f5aba0d5f53397dc615400e61aa69 |
LgRegv | # 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... | horsepurve/DeepVoro | LgRegv | false | 3,626 | [
"MIT"
] | 0 | 1b67a8e0d51e1c966a2af96d4b6a495f8390f608 | https://github.com/horsepurve/DeepVoro/tree/1b67a8e0d51e1c966a2af96d4b6a495f8390f608 |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | THE-RAF/Reinforcement-Learning | Network | false | 1,120 | [
"MIT"
] | 0 | 36b4c5330740b533fb8170263f995afb91a1d021 | https://github.com/THE-RAF/Reinforcement-Learning/tree/36b4c5330740b533fb8170263f995afb91a1d021 |
GraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.init import xavier_uniform_
assert_size_stri... | CSer-Tang-hao/FS-KTN | GraphConv | false | 7,873 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
PoolFormerBlock | import math
import torch
import warnings
import torch.nn as nn
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
"""Copy & paste from PyTorch official master until it's in a few official releases - RW
Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
"""
def n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | xwyzsn/solo-learn | PoolFormerBlock | false | 16,760 | [
"MIT"
] | 693 | 16d021d8053439a3de205337ab2a11d191500b09 | https://github.com/xwyzsn/solo-learn/tree/16d021d8053439a3de205337ab2a11d191500b09 |
MultiheadAttention | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.jit.quantized
import torch.onnx.operators
def combine_heads(X):
"""
Combine heads (the inverse of split heads):
1) Transpose X from (batch size, nheads, sequence length, d_head) ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ayansam1152/translate | MultiheadAttention | false | 13,398 | [
"BSD-3-Clause"
] | 748 | 33d397fc25fb1072abd2975c77c602a2d031c6c4 | https://github.com/Ayansam1152/translate/tree/33d397fc25fb1072abd2975c77c602a2d031c6c4 |
MutualInfoLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | yueyu-stu/EdgeAwareSpixel | MutualInfoLoss | false | 11,061 | [
"MIT"
] | 0 | f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 | https://github.com/yueyu-stu/EdgeAwareSpixel/tree/f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 |
Fusion | # 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 ... | hgrhgy/NumSeq2SQL | Fusion | false | 10,223 | [
"MIT"
] | 0 | 6f22fdf108736f979afa2dbd3af14aa9ad4718aa | https://github.com/hgrhgy/NumSeq2SQL/tree/6f22fdf108736f979afa2dbd3af14aa9ad4718aa |
GPT2Postprocessing | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class GPT2Postprocessing(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_epsilon)
self.lm_head = nn.Linear(config.hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | tunib-ai/large-scale-lm-tutorials | GPT2Postprocessing | false | 16,631 | [
"Apache-2.0"
] | 128 | ca29ff9f945a59abcc3e3f1000c4d83de97973d4 | https://github.com/tunib-ai/large-scale-lm-tutorials/tree/ca29ff9f945a59abcc3e3f1000c4d83de97973d4 |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dhananjaisharma10/mmdetection | h_swish | false | 12,265 | [
"Apache-2.0"
] | 0 | 6f6db3211c3760cffe9db2350297c42cc29ce140 | https://github.com/dhananjaisharma10/mmdetection/tree/6f6db3211c3760cffe9db2350297c42cc29ce140 |
DilatedResidualLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cmhungsteve/SSTDA | DilatedResidualLayer | false | 15,045 | [
"MIT"
] | 154 | 9c5e1df952bd122ea474046d91e3ac6fa79ec312 | https://github.com/cmhungsteve/SSTDA/tree/9c5e1df952bd122ea474046d91e3ac6fa79ec312 |
NormedLinear | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Parameter
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, 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.... | IssacCyj/imbalanced-semi-self | NormedLinear | false | 2,397 | [
"MIT"
] | 0 | 33ef166532c94c7ac65b41238c751b0a5369262b | https://github.com/IssacCyj/imbalanced-semi-self/tree/33ef166532c94c7ac65b41238c751b0a5369262b |
MobileNetV3Classifier | # 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 ... | iofthetiger/pkuad | MobileNetV3Classifier | false | 6,901 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
RpowFloat | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | NVIDIA-AI-IOT-private/torch2trt | RpowFloat | false | 10,535 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
adaILN | # 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.nn.parameter import Parameter
assert_size_stri... | denny3388/Conditional-UGATIT | adaILN | false | 1,833 | [
"MIT"
] | 0 | 86ad35f05aaa105a814dec031d37370f44b71d5b | https://github.com/denny3388/Conditional-UGATIT/tree/86ad35f05aaa105a814dec031d37370f44b71d5b |
MAB | # 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.... | KohavTal/SCAE_Project | MAB | false | 8,407 | [
"Apache-2.0"
] | 40 | bc6d1c3697fcb9327dd96e9657c3299b47cf355e | https://github.com/KohavTal/SCAE_Project/tree/bc6d1c3697fcb9327dd96e9657c3299b47cf355e |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | iliasprc/Compact-Transformers | TransformerEncoderLayer | false | 3,685 | [
"Apache-2.0"
] | 0 | 31975a0b4469854dfb0e0cbcedd8f0698cf84a7e | https://github.com/iliasprc/Compact-Transformers/tree/31975a0b4469854dfb0e0cbcedd8f0698cf84a7e |
qd | # 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... | IamWangYunKai/DG-TrajGen | qd | false | 8,278 | [
"MIT"
] | 31 | 0a8aab7e1c05111a5afe43d53801c55942e9ff56 | https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56 |
AvgPool | import torch
import torch.nn as nn
class AvgPool(nn.Module):
"""Module for MaxPool conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(AvgPool, self).__init__()
self.conv2d = nn.Conv2d(inp, out, kernel_size=kernel_size, bias=bias)
self.pool =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AliaksandrSiarohin/pytorch2keras | AvgPool | false | 8,904 | [
"MIT"
] | 0 | 9c8ee213cff43ade152b1de78fa76fd05ec8b40a | https://github.com/AliaksandrSiarohin/pytorch2keras/tree/9c8ee213cff43ade152b1de78fa76fd05ec8b40a |
_BahdanauAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | VarnithChordia/Multlingual_Punctuation_restoration | _BahdanauAttention | false | 18,048 | [
"MIT"
] | 8 | 17c026e8935b9fecae01d446a756926c7733fcd1 | https://github.com/VarnithChordia/Multlingual_Punctuation_restoration/tree/17c026e8935b9fecae01d446a756926c7733fcd1 |
SANet | # 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.... | HalbertCH/IEContraAST | SANet | false | 8,218 | [
"MIT"
] | 39 | 50ee949f5302a7e4a3cae3226610c03462093c21 | https://github.com/HalbertCH/IEContraAST/tree/50ee949f5302a7e4a3cae3226610c03462093c21 |
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... | ammarhusain/cs224u | TorchGloVeLoss | false | 6,180 | [
"Apache-2.0"
] | 1 | bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 | https://github.com/ammarhusain/cs224u/tree/bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 |
TwoLinearsModel | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class TwoLinearsModel(nn.Module):
def __init__(self, per_sample_shape: 'list', hidden_size: 'int',
output_size: 'int'):
super(TwoLinearsModel,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | arjunsuresh/aimet | TwoLinearsModel | false | 12,652 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
Attention | import math
import torch
from torch import nn
from torch.nn import functional as F
from typing import *
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, 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.... | HughMun/MultiBench | Attention | false | 13,786 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
ResidualBlockNoBN | import torch
import torch.utils.data
from torch.utils import data as data
import torch.nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_list, sc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | BCV-Uniandes/RSR | ResidualBlockNoBN | false | 8,128 | [
"zlib-acknowledgement"
] | 14 | dad60eedd3560f2655e3d1ed444153ed2616af2e | https://github.com/BCV-Uniandes/RSR/tree/dad60eedd3560f2655e3d1ed444153ed2616af2e |
FeatureWiseAffine | import torch
class BaseModule(torch.nn.Module):
def __init__(self):
super(BaseModule, self).__init__()
@property
def nparams(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
class FeatureWiseAffine(BaseModule):
def __init__(self):
super(FeatureWis... | 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... | Seungwoo0326/WaveGrad2-1 | FeatureWiseAffine | false | 14,391 | [
"MIT"
] | 45 | 3b202201348449b89353f28bce1596ca7939a810 | https://github.com/Seungwoo0326/WaveGrad2-1/tree/3b202201348449b89353f28bce1596ca7939a810 |
BertTextPooler | # 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_... | aditya10/vilbert-multi-task | BertTextPooler | false | 11,138 | [
"MIT"
] | 0 | dda8c16187ac6cc4f6266a823fbde528f65af720 | https://github.com/aditya10/vilbert-multi-task/tree/dda8c16187ac6cc4f6266a823fbde528f65af720 |
LabelwiseLinearOutput | import torch
import torch.nn as nn
class LabelwiseLinearOutput(nn.Module):
"""Applies a linear transformation to the incoming data for each label
Args:
input_size (int): The number of expected features in the input.
num_classes (int): Total number of classes.
"""
def __init__(self, i... | 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... | JamesLYC88/LibMultiLabel | LabelwiseLinearOutput | false | 9,155 | [
"MIT"
] | 0 | 042b76b3564409d916cf735ace617319009ae118 | https://github.com/JamesLYC88/LibMultiLabel/tree/042b76b3564409d916cf735ace617319009ae118 |
BinaryCrossEntropy2D | import torch
import torch.nn as nn
from torch.nn.modules.loss import _WeightedLoss
class BinaryCrossEntropy2D(_WeightedLoss):
"""
Standard pytorch weighted nn.CrossEntropyLoss
"""
def __init__(self):
super(BinaryCrossEntropy2D, self).__init__()
self.nll_loss = nn.BCELoss(reduction='no... | 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... | JHalili/KidneySegQuicknat | BinaryCrossEntropy2D | false | 580 | [
"MIT"
] | 0 | 4ddc30f2cf935045bf6482a73a3e86e2d8da3696 | https://github.com/JHalili/KidneySegQuicknat/tree/4ddc30f2cf935045bf6482a73a3e86e2d8da3696 |
SilogLoss | import torch
import torch.nn as nn
class SilogLoss(nn.Module):
def __init__(self, ratio=10, ratio2=0.85):
super().__init__()
self.ratio = ratio
self.ratio2 = ratio2
def forward(self, pred, gt):
log_diff = torch.log(pred * self.ratio) - torch.log(gt * self.ratio)
silog... | 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... | pection/packnet-sfm | SilogLoss | false | 7,443 | [
"MIT"
] | 1 | d5673567b649e6bfda292c894cacdeb06aa80913 | https://github.com/pection/packnet-sfm/tree/d5673567b649e6bfda292c894cacdeb06aa80913 |
TNPG | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def flat_grad(grads):
grad_flatten = []
for grad in grads:
grad_flatten.append(grad.view(-1))
grad_flatten = torch.cat(grad_flatten)
return grad_flatten
def flat_hessian(hessians):
hessians_flatten = []... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | g6ling/Pytorch-Cartpole | TNPG | false | 15,387 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
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