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 |
|---|---|---|---|---|---|---|---|---|---|---|
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | booiljung/torchtutorials | GramMatrix | false | 3,231 | [
"MIT"
] | 0 | 827b1bcd0b701c573d7423de277d78a36f6e20d8 | https://github.com/booiljung/torchtutorials/tree/827b1bcd0b701c573d7423de277d78a36f6e20d8 |
GeneralSoftmax | # 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 enum
import fun... | ashutosh1919/neuro-symbolic-sudoku-solver | GeneralSoftmax | false | 14,908 | [
"Apache-2.0"
] | 52 | ecb4274ff66d3b6a86f64584e0a767bf785f107f | https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver/tree/ecb4274ff66d3b6a86f64584e0a767bf785f107f |
DQMLP | import torch
import torch.nn as nn
class DQMLP(nn.Module):
def __init__(self, n_observations, n_actions, n_hidden):
super().__init__()
self.linear = nn.Linear(n_observations, n_hidden)
self.linear_adv = nn.Linear(n_hidden, n_actions)
self.linear_value = nn.Linear(n_hidden, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Purple-PI/rlstructures | DQMLP | false | 14,246 | [
"MIT"
] | 281 | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | https://github.com/Purple-PI/rlstructures/tree/9b201b083715bbda2f3534b010c84e11dfc0a1c7 |
GrayscaleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | GuYuanjie/DeepFusionPrior | GrayscaleLoss | false | 5,231 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
Gaussian | import torch
from torch import Tensor
import torch.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
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 math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | yangyinuo823/torchani | Gaussian | false | 16,848 | [
"MIT"
] | 305 | b0cd62eda59829d197b3c37f2215ba1af64f1c8d | https://github.com/yangyinuo823/torchani/tree/b0cd62eda59829d197b3c37f2215ba1af64f1c8d |
QNet | import torch
import torch.nn as nn
class QNet(nn.Module):
def __init__(self, in_size: 'int', out_size: 'int'):
super(QNet, self).__init__()
self.fc1 = nn.Linear(in_size, 16)
self.fc_out = nn.Linear(16, out_size)
self.act = nn.LeakyReLU()
def forward(self, x):
o1 = sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | rosebin/gymlab | QNet | false | 4,200 | [
"BSD-3-Clause"
] | 0 | de97fc24e0ddf5e328a2aa732cc339b2371d92d1 | https://github.com/rosebin/gymlab/tree/de97fc24e0ddf5e328a2aa732cc339b2371d92d1 |
MLPEncoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLPEncoder(nn.Module):
def __init__(self, d_in, d_out):
super(MLPEncoder, self).__init__()
H1 = 100
H2 = 10
self.d_in = d_in
self.l1 = nn.Linear(d_in, H1)
self.l11 = nn.Linear(H1, H2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cheng-xie/motionEncode | MLPEncoder | false | 1,672 | [
"MIT"
] | 0 | fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | https://github.com/cheng-xie/motionEncode/tree/fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 |
UnaryMinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class UnaryMinModule(torch.nn.Module):
def __init__(self):
super(UnaryMinModule, self).__init__()
def forward(self, a):
return torch.min(a + a)
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 import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | mciprian13/glow | UnaryMinModule | false | 4,000 | [
"Apache-2.0"
] | 0 | 90f88205d9bf8baff8df5bbda51c9d138e3e668b | https://github.com/mciprian13/glow/tree/90f88205d9bf8baff8df5bbda51c9d138e3e668b |
ConvTanh | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | irsisyphus/reconet | ConvTanh | false | 15,605 | [
"MIT"
] | 56 | 863acf8dde4d45c8521634af27878fe04f3b2e56 | https://github.com/irsisyphus/reconet/tree/863acf8dde4d45c8521634af27878fe04f3b2e56 |
DilatedResidualLayer | # 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_... | LalehSamadfam/tcn-isba | DilatedResidualLayer | false | 2,494 | [
"MIT"
] | 0 | cd2d2c27723e77ba658c695b8b0ba64e4835acf4 | https://github.com/LalehSamadfam/tcn-isba/tree/cd2d2c27723e77ba658c695b8b0ba64e4835acf4 |
Critic | import torch
import numpy as np
from torch import nn
import torch.autograd
def fanin_(size):
fan_in = size[0]
weight = 1.0 / np.sqrt(fan_in)
return torch.Tensor(size).uniform_(-weight, weight)
class Critic(nn.Module):
def __init__(self, state_dim, action_dim, h1=64, h2=32, init_w=0.003):
su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | ori-goals/lfd-min-human-effort | Critic | false | 7,429 | [
"MIT"
] | 1 | f9fd70cdeb661151e5f81ac538ceb865531146b9 | https://github.com/ori-goals/lfd-min-human-effort/tree/f9fd70cdeb661151e5f81ac538ceb865531146b9 |
KLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
def kl_loss(x, y):
x = F.softmax(x.detach(), dim=1)
y = F.log_softmax(y, dim=1)
return torch.mean(torch.sum(x * (torch.log(x) - y), dim=1))
class KLLoss(nn.Module):
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | anurag1paul/pseudo_lidar | KLLoss | false | 6,224 | [
"MIT"
] | 1 | 02faf327efd43c986629d0ea797b058e464c05aa | https://github.com/anurag1paul/pseudo_lidar/tree/02faf327efd43c986629d0ea797b058e464c05aa |
GlobalAttention_text | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class GlobalAttention_text(nn.Module):
def __init__(self, idf, cdf):
super(GlobalAttention_text, self).__init__()
self.conv_context = nn.Conv1d(cdf, idf, kernel_size=1, stride=1,
padding=0)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Creling/DM-GAN | GlobalAttention_text | false | 2,126 | [
"MIT"
] | 0 | ec2ce6d7fae4cf3ba2099b3db09926e544b2b759 | https://github.com/Creling/DM-GAN/tree/ec2ce6d7fae4cf3ba2099b3db09926e544b2b759 |
HDRLoss | import torch
from torch import nn
from numpy import *
from math import sqrt as sqrt
from itertools import product as product
class HDRLoss(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super(HDRLoss, 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
from torch._inductor.runtime import triton_helpers
from torch import nn
from numpy import *
from math import sqrt as sqrt
from itertools imp... | davidpqc1231/AnnotatedNetworkModelGit | HDRLoss | false | 6,549 | [
"MIT"
] | 1 | 419e6c9ef31f1efe7fd63d693b12c08a7d8c0f33 | https://github.com/davidpqc1231/AnnotatedNetworkModelGit/tree/419e6c9ef31f1efe7fd63d693b12c08a7d8c0f33 |
Generator | import torch
import torch.distributed
import torch
import torch.nn as nn
def gumbel_softmax(logits, tau=1.0, hard=False, log_mode=True, dim=-1):
while True:
gumbels = -torch.empty_like(logits).exponential_().log()
gumbels = (logits + gumbels) / tau
if log_mode:
y_soft = gumbels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RowitZou/RankAE | Generator | false | 8,727 | [
"MIT"
] | 23 | d47ab58aa4fda203c551e36cbe04edd564b76d89 | https://github.com/RowitZou/RankAE/tree/d47ab58aa4fda203c551e36cbe04edd564b76d89 |
NormalAttention_embedded_gaussian | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Schwartz-Zha/My-invertible-resnet | NormalAttention_embedded_gaussian | false | 1,039 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
Binarizer | import torch
from abc import ABC
from sklearn.preprocessing import Binarizer
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detecti... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kvenkman/hummingbird | Binarizer | false | 4,081 | [
"MIT"
] | 0 | dac08f4ff4a4103df4a8e83329a02f2d804bf34d | https://github.com/kvenkman/hummingbird/tree/dac08f4ff4a4103df4a8e83329a02f2d804bf34d |
ConvBnRelu | # 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.... | SkywalkerAtlas/HRGAN | ConvBnRelu | false | 5,835 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
LocationEncoder | # 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.... | hk19960522/2018-DL-Final | LocationEncoder | false | 3,604 | [
"MIT"
] | 0 | cbc70260aa22d7df366a1d28bee472f1fc5b82c7 | https://github.com/hk19960522/2018-DL-Final/tree/cbc70260aa22d7df366a1d28bee472f1fc5b82c7 |
SSIM | import torch
import torch.nn as nn
class SSIM(nn.Module):
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_... | 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
... | ArminMasoumian/GCNDepth | SSIM | false | 7,726 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
Conv2dSameExport | # 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.utils.data
import torch.utils.data.distributed
from torch import nn... | Adlik/zen_nas | Conv2dSameExport | false | 16,901 | [
"Apache-2.0"
] | 7 | d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | https://github.com/Adlik/zen_nas/tree/d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 |
UpsampleConv | # 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.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | UpsampleConv | false | 16,620 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
Tacotron2Loss | import torch
import torch.utils.data
from torch import nn
class Tacotron2Loss(nn.Module):
def __init__(self):
super(Tacotron2Loss, self).__init__()
def forward(self, model_output, targets):
mel_target, gate_target = targets[0], targets[1]
mel_out_before, mel_out_after, gate_out, _ = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | leijue222/tacotron2 | Tacotron2Loss | false | 15,891 | [
"BSD-3-Clause"
] | 93 | 5950728a91e7a9355f42f658e00db2a2aef94247 | https://github.com/leijue222/tacotron2/tree/5950728a91e7a9355f42f658e00db2a2aef94247 |
StyledSiren | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | Dolorousrtur/style-people | StyledSiren | false | 8,012 | [
"MIT"
] | 15 | c48b12b245cc50f8230c0654dffe40016f2a69f1 | https://github.com/Dolorousrtur/style-people/tree/c48b12b245cc50f8230c0654dffe40016f2a69f1 |
Critic | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from math import *
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | Jeyhooon/deep-reinforcement-learning | Critic | false | 651 | [
"MIT"
] | 0 | 7a6f1974493a2058635539a4868512cdf3fb5bdb | https://github.com/Jeyhooon/deep-reinforcement-learning/tree/7a6f1974493a2058635539a4868512cdf3fb5bdb |
L1_log | import torch
import torch.nn as nn
import torch.nn.functional as F
class L1_log(nn.Module):
def __init__(self):
super(L1_log, self).__init__()
def forward(self, fake, real):
if not fake.shape == real.shape:
_, _, H, W = real.shape
fake = F.upsample(fake, size=(H, W), ... | 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
... | d4l3k/crowds | L1_log | false | 12,243 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
LastLevelMaxPool | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
import torchvision.transforms.functional as F
from torch.nn import functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | BorisLestsov/retinamask | LastLevelMaxPool | false | 13,765 | [
"MIT"
] | 706 | 265a65f018c64220bcea946d306fc7b07a692b16 | https://github.com/BorisLestsov/retinamask/tree/265a65f018c64220bcea946d306fc7b07a692b16 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, h2):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Saran-nns/delve | Net | false | 1,032 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
PixelNorm | import torch
import torch.nn as nn
class PixelNorm(nn.Module):
def __init__(self):
super(PixelNorm, self).__init__()
self.epsilon = 1e-08
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) +
self.epsilon)
def get_inputs():
return [to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AjaybirRandhawa/Face-Generator | PixelNorm | false | 18,399 | [
"Apache-2.0"
] | 2 | 9cac0822b6e6337c3599e949154ce44eeae5746b | https://github.com/AjaybirRandhawa/Face-Generator/tree/9cac0822b6e6337c3599e949154ce44eeae5746b |
QuantConv2d | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class Round(Function):
@staticmethod
def forward(self, input):
sign = torch.sign(input)
output = sign * torch.floor(torch.abs(input) + 0.5)
return output
@staticmethod
def 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XueYue404/QNN | QuantConv2d | false | 1,261 | [
"MIT"
] | 0 | 43cea970404156b591088d77672df58261edf1eb | https://github.com/XueYue404/QNN/tree/43cea970404156b591088d77672df58261edf1eb |
MatchingNetwork | # 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.... | nikran1/Few_shot | MatchingNetwork | false | 16,180 | [
"MIT"
] | 497 | 5298c98e208411e44ee7767e6f4d457006d373cb | https://github.com/nikran1/Few_shot/tree/5298c98e208411e44ee7767e6f4d457006d373cb |
ConvReLU2 | # 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 math
import torch.nn.functional as F
from torch.nn import Conv2d
from tor... | pc2005/MonoRec | ConvReLU2 | false | 12,873 | [
"MIT"
] | 0 | 6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c | https://github.com/pc2005/MonoRec/tree/6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ahmad1s/FastSpeech2 | ScaledDotProductAttention | false | 8,848 | [
"MIT"
] | 0 | d31802ffcd74bb2c2ca57b53e481917989ded6b9 | https://github.com/Ahmad1s/FastSpeech2/tree/d31802ffcd74bb2c2ca57b53e481917989ded6b9 |
PredictionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | chenkkkk/DeepCTR-PyTorch | PredictionLayer | false | 6,430 | [
"Apache-2.0"
] | 1 | a10a3ace4ad79171e7fb182407b3e4d22bf753e7 | https://github.com/chenkkkk/DeepCTR-PyTorch/tree/a10a3ace4ad79171e7fb182407b3e4d22bf753e7 |
ChannelNorm | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class ChannelNorm(Module):
"""
## Channel Normalization
This is similar to [Group Normalization](../group_norm/index.html) but affine transform is done group wise.
""... | 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.nn import Module
from torch import nn
import torch.utils.data
import... | mcx/annotated_deep_learning_paper_implementations | ChannelNorm | false | 7,191 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
DotRole | # 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
assert_size_stride = torch._C._dynamo.g... | jk96491/SMAC | DotRole | false | 15,702 | [
"Apache-2.0"
] | 64 | 7aaf4673b0eecafc4ab25f381eea20fc762af56a | https://github.com/jk96491/SMAC/tree/7aaf4673b0eecafc4ab25f381eea20fc762af56a |
MultiHeadQKVAttention | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | MultiHeadQKVAttention | false | 10,423 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 |
CasualConv1d | # 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... | Asichurter/Few-Shot-Project | CasualConv1d | false | 80 | [
"MIT"
] | 0 | 865cd6aa7b996c518dfa48dcc9ffad90445f9efe | https://github.com/Asichurter/Few-Shot-Project/tree/865cd6aa7b996c518dfa48dcc9ffad90445f9efe |
PatchEmbeddings | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | PatchEmbeddings | false | 16,573 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
ConvMeanPool | # 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... | Sriram-Ravula/ncsnv2 | ConvMeanPool | false | 2,856 | [
"MIT"
] | 0 | f610b59441a34063fae1c02aa06837b7eec95c03 | https://github.com/Sriram-Ravula/ncsnv2/tree/f610b59441a34063fae1c02aa06837b7eec95c03 |
SelfAttention | # 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.... | felixyu7/perceiver-io-1 | SelfAttention | false | 3,540 | [
"Apache-2.0"
] | 0 | 895f09e75e5a4b5e90dfef5d3a86ea26c2f48f4e | https://github.com/felixyu7/perceiver-io-1/tree/895f09e75e5a4b5e90dfef5d3a86ea26c2f48f4e |
SmoothCrossEntropyLoss | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
def cross_entropy(input, target, size_average=True):
""" Cross entropy that accepts soft targets
Args:
pred: predictions for neural network
targets: targets, can be soft
size_average: if false, sum 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import M... | AustinCai/gmaxup-augmentation | SmoothCrossEntropyLoss | false | 77 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
BCELoss2d | import torch
import torch.nn.functional as F
import torch.nn as nn
class BCELoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
super(BCELoss2d, self).__init__()
self.bce_loss = nn.BCELoss(weight, size_average)
def forward(self, logits, targets):
probs = F.sigmoid(... | 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... | cmarasinou/carvana-challenge | BCELoss2d | false | 15,049 | [
"MIT"
] | 93 | 4e1c43f306cfbef1df267acfce59bdcf19504850 | https://github.com/cmarasinou/carvana-challenge/tree/4e1c43f306cfbef1df267acfce59bdcf19504850 |
GELU | import torch
from torch import nn
class GELU(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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... | simonlevine/enformer-pytorch | GELU | false | 13,093 | [
"MIT"
] | 0 | 342915c3f9385f5f24ee4d1d9965d126d49ca279 | https://github.com/simonlevine/enformer-pytorch/tree/342915c3f9385f5f24ee4d1d9965d126d49ca279 |
MultiHeadSelfAttention | # 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.... | myoons/image-gpt-pytorch | MultiHeadSelfAttention | false | 7,318 | [
"Apache-2.0"
] | 1 | d05081250d01ce208796dfb246ea1c9a093237c5 | https://github.com/myoons/image-gpt-pytorch/tree/d05081250d01ce208796dfb246ea1c9a093237c5 |
BCEWithLogitsLoss | # 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
from torch ... | theleokul/Real-ESRGAN | BCEWithLogitsLoss | false | 4,419 | [
"BSD-3-Clause"
] | 0 | 0afbc090d012d729e6cb3ff47a80018d53bce3f6 | https://github.com/theleokul/Real-ESRGAN/tree/0afbc090d012d729e6cb3ff47a80018d53bce3f6 |
ExternalAttention | import torch
from torch import nn
from torch.nn import init
class ExternalAttention(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LiChengChen666/DetectDee | ExternalAttention | false | 9,814 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, in_dim, out_dim):
super(MLP, self).__init__()
out = max(8, in_dim * 2)
self.input = nn.Linear(in_dim, out)
self.fc = nn.Linear(out, out)
self.fc2 = nn.Linear(out, out)
self.output = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Malta-Lab/IUPE | MLP | false | 8,541 | [
"MIT"
] | 10 | 44ddf119917538f02bb69509fec7a8314eed419f | https://github.com/Malta-Lab/IUPE/tree/44ddf119917538f02bb69509fec7a8314eed419f |
SelfAttention0 | # 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.... | SSussexGit/deepikachu | SelfAttention0 | false | 2,807 | [
"MIT"
] | 0 | 72999c4a3f1767c3e5f332fe64cba9240ef43a79 | https://github.com/SSussexGit/deepikachu/tree/72999c4a3f1767c3e5f332fe64cba9240ef43a79 |
MomentumNetSide | # 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
import torch.utils.data.dataloader
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | ClashLuke/HomebrewNLP | MomentumNetSide | false | 281 | [
"BSD-2-Clause"
] | 0 | 18d9a9a32af4e5e5672a9261ef6ac613dc9194c0 | https://github.com/ClashLuke/HomebrewNLP/tree/18d9a9a32af4e5e5672a9261ef6ac613dc9194c0 |
Classifier | import torch
from torch import nn
import torch.nn.functional as F
class Classifier(nn.Module):
def __init__(self, input_size):
super().__init__()
self.hidden_1 = nn.Linear(input_size, 100)
self.hidden_2 = nn.Linear(100, 100)
self.hidden_3 = nn.Linear(100, 50)
self.hidden_4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChengJiacheng/Applied-Deep-Learning-with-PyTorch | Classifier | false | 8,901 | [
"MIT"
] | 0 | 260d3ad3929705f615c758dd72f9539f390461bf | https://github.com/ChengJiacheng/Applied-Deep-Learning-with-PyTorch/tree/260d3ad3929705f615c758dd72f9539f390461bf |
MAP_Linear_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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | andrewfoongyk/cs230-code-examples | MAP_Linear_Layer | false | 1,431 | [
"MIT"
] | 0 | 8e12aa3414bdada6ec6002bedf919a6816ba237c | https://github.com/andrewfoongyk/cs230-code-examples/tree/8e12aa3414bdada6ec6002bedf919a6816ba237c |
TrainablePositionalEncoding | # 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_... | Worm4047/TVR | TrainablePositionalEncoding | false | 14,604 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
SEModule | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
from time import *
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, 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.nn import Module
i... | BillKerman/FaceNetCustomized | SEModule | false | 17,003 | [
"MIT"
] | 4 | 30bb99b62f960034c4aa4206d7dc22de672a997f | https://github.com/BillKerman/FaceNetCustomized/tree/30bb99b62f960034c4aa4206d7dc22de672a997f |
SumNormalizer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | KevinMusgrave/pytorch-adapt | SumNormalizer | false | 13,948 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
Sine | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel... | doansangg/CGAN-PyTorch | Sine | false | 6,578 | [
"Apache-2.0"
] | 1 | 941f5bd75102bed7f2eccd7feb9af8e6134af0e4 | https://github.com/doansangg/CGAN-PyTorch/tree/941f5bd75102bed7f2eccd7feb9af8e6134af0e4 |
Foo | # 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.jit
import torch... | andreas-hommel/glow | Foo | false | 3,336 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Scale | import torch
import torch.utils.data
from torch import nn
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | CV-Rookie/EmbedMask | Scale | false | 4,947 | [
"MIT"
] | 1 | 3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 | https://github.com/CV-Rookie/EmbedMask/tree/3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 |
AE_Net | import torch
from torch.optim import *
from torch import nn
class AE_Net(nn.Module):
"""docstring for AE_Net."""
def __init__(self, input_shape):
super(AE_Net, self).__init__()
self.encoder_hidden_layer = nn.Linear(in_features=input_shape,
out_features=128)
self.encoder_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.optim import *
fro... | Paratra/IoTAnalytics_pub | AE_Net | false | 926 | [
"MIT"
] | 0 | 8c1d02b60ef609c3cba654ce4a5568c39fc63edf | https://github.com/Paratra/IoTAnalytics_pub/tree/8c1d02b60ef609c3cba654ce4a5568c39fc63edf |
PositionWiseFeedForward | # 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.... | Lhx94As/PHO-LID | PositionWiseFeedForward | false | 5,540 | [
"MIT"
] | 1 | 44843b25b977dd6e0b77b520dbe3f2ff1ea633cd | https://github.com/Lhx94As/PHO-LID/tree/44843b25b977dd6e0b77b520dbe3f2ff1ea633cd |
DeepHeadModule | # 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_... | DannyDannyDanny/DeepPrivacy | DeepHeadModule | false | 2,132 | [
"MIT"
] | 0 | 749e260bdcc28a0c12d526f24e4f5315d1b447ad | https://github.com/DannyDannyDanny/DeepPrivacy/tree/749e260bdcc28a0c12d526f24e4f5315d1b447ad |
SpatialAttentionModule | import torch
import torch.nn as nn
class SpatialAttentionModule(nn.Module):
def __init__(self):
super(SpatialAttentionModule, self).__init__()
self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=
7, stride=1, padding=3)
self.sigmoid = nn.Sigmoid()
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | poppy862/Qnet | SpatialAttentionModule | false | 4,128 | [
"Apache-2.0"
] | 0 | da751bc6eb9ae23e0ff9b96fe0afdfd6bed31f8b | https://github.com/poppy862/Qnet/tree/da751bc6eb9ae23e0ff9b96fe0afdfd6bed31f8b |
GroupPointWise | import torch
import torch.nn as nn
class GroupPointWise(nn.Module):
def __init__(self, in_dim, n_heads=4, proj_factor=1, target_dim=None):
super().__init__()
if target_dim is not None:
proj_ch = target_dim // proj_factor
else:
proj_ch = in_dim // proj_factor
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | nachiket273/VisTrans | GroupPointWise | false | 7,309 | [
"MIT"
] | 1 | 99129b02f275424ebff900189ec2055f26bb9912 | https://github.com/nachiket273/VisTrans/tree/99129b02f275424ebff900189ec2055f26bb9912 |
BoundPow | # 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 math
import numpy as np
import torch.nn as nn
import torch.nn.functional... | Mahoumaru/auto_LiRPA | BoundPow | false | 13,222 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
LogisticLoss | # 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
from torch.... | MacOS/torchkge | LogisticLoss | false | 13,998 | [
"BSD-3-Clause"
] | 248 | 89ed724368f3a5279c0f79c6ba1f948ed2a5696f | https://github.com/MacOS/torchkge/tree/89ed724368f3a5279c0f79c6ba1f948ed2a5696f |
MulScalarNegative | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class MulScalarNegative(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = ... | 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.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyn... | T-head-Semi/tvm | MulScalarNegative | false | 17,967 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 |
CELoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class MetricLearningLoss(nn.Module):
"""
Generic loss function to be used in a metric learning setting
"""
def __init__(self, embedding_size, n_classes, device='cpu', *args, **kwargs
):
super(MetricLearningLoss, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Wadaboa/titanet | CELoss | false | 18,084 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea |
My_SmoothL1Loss | import torch
class My_SmoothL1Loss(torch.nn.Module):
def __init__(self):
super(My_SmoothL1Loss, self).__init__()
def forward(self, x, y):
total_loss = 0
assert x.shape == y.shape
z = (x - y).float()
mse_mask = (torch.abs(z) < 0.01).float()
l1_mask = (torch.abs... | 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... | theleokul/AWR-Adaptive-Weighting-Regression | My_SmoothL1Loss | false | 4,415 | [
"MIT"
] | 0 | a6c224302bab474db8b774a2d009c9497e32f6bd | https://github.com/theleokul/AWR-Adaptive-Weighting-Regression/tree/a6c224302bab474db8b774a2d009c9497e32f6bd |
UniformBatchMiner | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
from typing import Any
from typing import Dict
from typing import Lis... | p768lwy3/torecsys | UniformBatchMiner | false | 16,219 | [
"MIT"
] | 92 | 2251366268b4fbe6f8c3ab1628fa72a0db043dcd | https://github.com/p768lwy3/torecsys/tree/2251366268b4fbe6f8c3ab1628fa72a0db043dcd |
CoordFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | PeterouZh/CIPS-3D | CoordFC | false | 14,160 | [
"MIT"
] | 308 | 9b8bfa0fb23f642af042e150ccd70408f9d137c6 | https://github.com/PeterouZh/CIPS-3D/tree/9b8bfa0fb23f642af042e150ccd70408f9d137c6 |
ClusterDistance | import torch
from torch import nn
from typing import Optional
class ClusterDistance(nn.Module):
def __init__(self, n_classes: 'int', enc_shape: 'int', cluster_centers:
'Optional[torch.Tensor]'=None) ->None:
"""
:param n_classes: number of clusters
:param enc_shape: embedding dime... | 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 typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | marx-alex/Morphelia | ClusterDistance | false | 3,977 | [
"MIT"
] | 0 | 809278b07f1a535789455d54df3cbddc850d609c | https://github.com/marx-alex/Morphelia/tree/809278b07f1a535789455d54df3cbddc850d609c |
BlockWidth1d | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class BlockWidth1d(nn.Module):
def __init__(self, width) ->None:
super().__init__()
self.conv = nn.Conv1d(width, width, kernel_size=5, padding=2)
def forward(self, x):
x = x + F.leaky_relu(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.nn as nn
assert_size_stride = torch._C._dyn... | ishine/HiFiplusplus-pytorch | BlockWidth1d | false | 15,613 | [
"MIT"
] | 69 | 8be0d0e0092d4f609c37bfbeede5a9ad9bd7470a | https://github.com/ishine/HiFiplusplus-pytorch/tree/8be0d0e0092d4f609c37bfbeede5a9ad9bd7470a |
PolicyNetwork | # 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... | SAMMiCA/DL_based_E2E_Driving | PolicyNetwork | false | 17,881 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
focal_loss | import torch
import torch.nn.functional as F
class focal_loss(torch.nn.Module):
"""
Loss function for classification tasks with
large data imbalance. Focal loss (FL) is define as:
FL(p_t) = -alpha*((1-p_t)^gamma))*log(p_t),
where p_t is a cross-entropy loss for binary classification.
For more... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | miguel-fc/atomai | focal_loss | false | 10,661 | [
"MIT"
] | 0 | f51699ef5e1bfc577781977d38f7414b1b51449d | https://github.com/miguel-fc/atomai/tree/f51699ef5e1bfc577781977d38f7414b1b51449d |
LeCunTanh | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | fmhoward/pysurvival | LeCunTanh | false | 12,404 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
FakeRKHSConvNet | import math
import torch
import numpy as np
from torch import nn as nn
from torch import optim as optim
from math import *
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super(MaybeBatchNorm2d, self).__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SNUHDR2018/ConSSL | FakeRKHSConvNet | false | 14,359 | [
"MIT"
] | 78 | c7d406d0224e38895986c8fb7281a189e493c982 | https://github.com/SNUHDR2018/ConSSL/tree/c7d406d0224e38895986c8fb7281a189e493c982 |
EqualLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Asha-Gutlapalli/StyleGAN2-Art | EqualLinear | false | 16,950 | [
"MIT"
] | 4 | 5a8a8ad61183e82abafe587d755a7fbce28aa8f0 | https://github.com/Asha-Gutlapalli/StyleGAN2-Art/tree/5a8a8ad61183e82abafe587d755a7fbce28aa8f0 |
VocabGraphConvolution | # 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 math
import torch.nn as nn
import torch.nn.init as init
assert_size_strid... | JakobVokac/VGCN-BERT | VocabGraphConvolution | false | 9,150 | [
"MIT"
] | 0 | f82f1922c0d461c12d43c45bc58b61b92534b99b | https://github.com/JakobVokac/VGCN-BERT/tree/f82f1922c0d461c12d43c45bc58b61b92534b99b |
Enrichment | import torch
import torch.nn as nn
class Enrichment(nn.Module):
def __init__(self, c_in, rate=2):
super(Enrichment, self).__init__()
self.rate = rate
self.relu = nn.ReLU(inplace=True)
self.conv = nn.Conv2d(c_in, 32, 3, stride=1, padding=1)
dilation = self.rate * 1 if self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | xavysp/TIN_xsp | Enrichment | false | 4,582 | [
"MIT"
] | 0 | 9f68e03923f637f4d4ef885694dfc3aaaaad6cea | https://github.com/xavysp/TIN_xsp/tree/9f68e03923f637f4d4ef885694dfc3aaaaad6cea |
BCEWithLogitsLossWeighted | # 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... | FANG-Xiaolin/uois | BCEWithLogitsLossWeighted | false | 2,225 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
MLPComposition | import torch
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class CompositionFunction(torch.nn.Module):
def __init__(self, representation_size: 'int'):
super().__init__()
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
raise NotImplemented... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.par... | XeniaOhmer/SystematicRepresentations | MLPComposition | false | 1,242 | [
"MIT"
] | 0 | 825208d1be659dc820e61f577cdb53afc47302f4 | https://github.com/XeniaOhmer/SystematicRepresentations/tree/825208d1be659dc820e61f577cdb53afc47302f4 |
ECB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
from torch.nn import functional as F
from torch.nn im... | hyunobae/BasicSR | ECB | false | 12,533 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
CLSTMCell | # 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 ... | Rehan-Ahmar/UNet-Zoo | CLSTMCell | false | 14,282 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
SoftmaxDeepLiftModel | # 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.... | aravipati12/captum | SoftmaxDeepLiftModel | false | 10,111 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
FeedForwardActorNN | # 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.... | britig/policy-refinement-bo | FeedForwardActorNN | false | 6,366 | [
"MIT"
] | 1 | c8a1e347d6e27c991e945afae9b5d9b482806f4b | https://github.com/britig/policy-refinement-bo/tree/c8a1e347d6e27c991e945afae9b5d9b482806f4b |
UpsampleConvLayer | import torch
from torch import nn
import torch.nn.functional as f
import torch.nn.parallel
class UpsampleConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(UpsampleConvLayer, self).__init__()
bias = Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DA4EVENT/home | UpsampleConvLayer | false | 17,180 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
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_... | CUSP-NYU/autonomous-drone-swarm | Net | false | 192 | [
"MIT"
] | 0 | 3bad474fd9641521588af61ddc48b84ffc746a74 | https://github.com/CUSP-NYU/autonomous-drone-swarm/tree/3bad474fd9641521588af61ddc48b84ffc746a74 |
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.triton_helpers import libdevice
from torch import n... | batikim09/FloWaveNet | Conv | false | 14,943 | [
"MIT"
] | 499 | 791f51aff530b2af4f9aa0d9fcb4af53d28a0997 | https://github.com/batikim09/FloWaveNet/tree/791f51aff530b2af4f9aa0d9fcb4af53d28a0997 |
Resample | import torch
from torch import nn
from typing import Optional
class LinearStack(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
activation_fn: 'Optional[nn.Module]'=None, n: 'int'=1,
hidden_features: 'Optional[int]'=None, dropout: 'Optional[float]'=None
):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | IusztinPaul/yacht | Resample | false | 17,449 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 |
Smooth | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Smooth(nn.Module):
"""
<a id="smooth"></a>
### Smoothing Layer
This layer blurs each channel
"""
def __init__(self):
super().__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.utils.data
import torch.nn.functional
import t... | Aarsh2001/annotated_deep_learning_paper_implementations | Smooth | false | 4,793 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
MultiHeadedAttention | # 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.... | Slyne/wenet | MultiHeadedAttention | false | 9,507 | [
"Apache-2.0"
] | 0 | de74d8acf40f47a3c503bff5cf4ed6808a9dad14 | https://github.com/Slyne/wenet/tree/de74d8acf40f47a3c503bff5cf4ed6808a9dad14 |
IOU_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | allen-q/pytorch | IOU_Loss | false | 3,081 | [
"MIT"
] | 0 | 76947f8d6f0bcee04425ad69f93b9a5e3a5060ae | https://github.com/allen-q/pytorch/tree/76947f8d6f0bcee04425ad69f93b9a5e3a5060ae |
SigmaL1SmoothLoss | import torch
from torch import nn
class SigmaL1SmoothLoss(nn.Module):
def forward(self, pred, targ):
reg_diff = torch.abs(targ - pred)
reg_loss = torch.where(torch.le(reg_diff, 1 / 9), 4.5 * torch.pow(
reg_diff, 2), reg_diff - 1 / 18)
return reg_loss.mean()
def get_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | VrunArya/Hacktoberfest2021 | SigmaL1SmoothLoss | false | 5,941 | [
"MIT"
] | 1 | 5e739e52310dabf8b131abe5ecf906e13711b9d6 | https://github.com/VrunArya/Hacktoberfest2021/tree/5e739e52310dabf8b131abe5ecf906e13711b9d6 |
UpBlock | import torch
import torch.nn as nn
class UpBlock(nn.Module):
def __init__(self, in_f, out_f, stride=2, add_blur=False):
super(UpBlock, self).__init__()
self.shuffle = nn.ConvTranspose2d(in_f, out_f, kernel_size=3,
stride=stride, padding=0)
self.has_blur = add_blur
if s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Kash6/AnimeBot | UpBlock | false | 13,936 | [
"MIT"
] | 177 | 99c68bdb03501d6919669c4aabbb9fe5ea92ec8e | https://github.com/Kash6/AnimeBot/tree/99c68bdb03501d6919669c4aabbb9fe5ea92ec8e |
BCEBlurWithLogitsLoss | # 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... | AkshayGanesh/yolov5processor | BCEBlurWithLogitsLoss | false | 4,807 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad |
Gaussian_transform | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | BorgwardtLab/TOGL | Gaussian_transform | false | 17,009 | [
"BSD-3-Clause"
] | 6 | d0c986cf829ca6bbae1a23e5cdab1c99146503cd | https://github.com/BorgwardtLab/TOGL/tree/d0c986cf829ca6bbae1a23e5cdab1c99146503cd |
channel_selection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Cydia2018/ViT-cifar10-pruning | channel_selection | false | 7,927 | [
"MIT"
] | 18 | 7de250edb8639094355b86e19c8303e635ade026 | https://github.com/Cydia2018/ViT-cifar10-pruning/tree/7de250edb8639094355b86e19c8303e635ade026 |
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.... | RiTUAL-UH/style_NER | BahdanauAttention | false | 8,711 | [
"MIT"
] | 17 | 4bb206cb48a45cc71deea3eea249eeb266c019a4 | https://github.com/RiTUAL-UH/style_NER/tree/4bb206cb48a45cc71deea3eea249eeb266c019a4 |
DiceLoss | import torch
import torch.nn as nn
import torch.utils.data
class DiceLoss(nn.Module):
"""DICE loss.
"""
def __init__(self, size_average=True, reduce=True, smooth=100.0, power=1):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.reduce = reduce
self.power = power
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | KeeratKG/pytorch_connectomics | DiceLoss | false | 702 | [
"MIT"
] | 0 | ba168da6f077ccfbeffcd8936df90ba413895086 | https://github.com/KeeratKG/pytorch_connectomics/tree/ba168da6f077ccfbeffcd8936df90ba413895086 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gchrupala/lyz | MLP | false | 12,419 | [
"MIT"
] | 0 | e1e99899af65f6c4cb1fd77485f6fa61ba3500f5 | https://github.com/gchrupala/lyz/tree/e1e99899af65f6c4cb1fd77485f6fa61ba3500f5 |
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