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 |
|---|---|---|---|---|---|---|---|---|---|---|
SSP | # 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, math as tl_math
import numpy as np
from torch import nn
import torch.nn.functi... | PKUfjh/deepqmc | SSP | false | 14,138 | [
"MIT"
] | 224 | 2a948ce712dd4e40568aa35931527e6c874eba73 | https://github.com/PKUfjh/deepqmc/tree/2a948ce712dd4e40568aa35931527e6c874eba73 |
ResForward | # 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
import torch.distributed
assert_size_stride = torch._C._dyn... | Improbable-AI/curiosity_baselines | ResForward | false | 17,457 | [
"MIT"
] | 5 | 42dca92b2fb66c0790a72206bf48595d3b5b487f | https://github.com/Improbable-AI/curiosity_baselines/tree/42dca92b2fb66c0790a72206bf48595d3b5b487f |
Biaffine | import torch
from typing import Callable
from typing import Optional
from torch import nn
class Biaffine(nn.Module):
def __init__(self, in1_features: 'int', in2_features: 'int',
out_features: 'int', init_func: 'Optional[Callable]'=None) ->None:
super(Biaffine, self).__init__()
self.in1_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Callable
from typing import Optional
from torch import nn
ass... | Zzoay/dependency_representations | Biaffine | false | 3,002 | [
"Apache-2.0"
] | 0 | 7f4726629878aaf9bfee645fe1b11032df05c82e | https://github.com/Zzoay/dependency_representations/tree/7f4726629878aaf9bfee645fe1b11032df05c82e |
IIDIsotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^n ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | TinBacon/FastAutoAugmentation | IIDIsotropicGaussianUVLoss | false | 5,891 | [
"Apache-2.0"
] | 1 | 011e4e348fd9a937a29df11695dc71410f555d0a | https://github.com/TinBacon/FastAutoAugmentation/tree/011e4e348fd9a937a29df11695dc71410f555d0a |
CDM | # 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.... | arjunsesh/lrr-neurips | CDM | false | 6,246 | [
"MIT"
] | 1 | d78106daec1e729b02a0452f74a37bf004ed243c | https://github.com/arjunsesh/lrr-neurips/tree/d78106daec1e729b02a0452f74a37bf004ed243c |
Block | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Clayrisee/BanchelorsProject-FAS | Block | false | 320 | [
"MIT"
] | 0 | 3da199fb2e7be04eed7f28374ef753383511dbee | https://github.com/Clayrisee/BanchelorsProject-FAS/tree/3da199fb2e7be04eed7f28374ef753383511dbee |
Dueling_Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | FlickerNiko/ai_lib | Dueling_Critic | false | 13,693 | [
"MIT"
] | 99 | 7087d4569c9a827d35dd8735b55a080834d31a82 | https://github.com/FlickerNiko/ai_lib/tree/7087d4569c9a827d35dd8735b55a080834d31a82 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | neptune-ml/data-science-bowl-2018 | DiceLoss | false | 16,154 | [
"MIT"
] | 92 | 2f76f2fc9836e53ada16d1e084afa0108b119011 | https://github.com/neptune-ml/data-science-bowl-2018/tree/2f76f2fc9836e53ada16d1e084afa0108b119011 |
Conv2dBlock | # 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 ... | AllenPu/mbdg | Conv2dBlock | false | 7,687 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
FirstNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Koukyosyumei/AIJack | FirstNet | false | 8,442 | [
"MIT"
] | 24 | 9545d3828907b54965ede85e0e12cb32eef54294 | https://github.com/Koukyosyumei/AIJack/tree/9545d3828907b54965ede85e0e12cb32eef54294 |
Encoder5 | # 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.... | EndyWon/Texture-Reformer | Encoder5 | false | 8,220 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
ResidualDenseBlock_3C | import torch
import torch.nn as nn
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, torch.nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C... | wsdea/EfficientSR | ResidualDenseBlock_3C | false | 4,554 | [
"MIT"
] | 0 | 077dea18c90e0d5bed722c609a776033c09f80e6 | https://github.com/wsdea/EfficientSR/tree/077dea18c90e0d5bed722c609a776033c09f80e6 |
SecondOrderInteraction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Parameter
assert_size_stride = ... | DanielMorales9/FactorizationPyTorch | SecondOrderInteraction | false | 17,200 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
Feedforward | import torch
from torch import nn
class Feedforward(torch.nn.Module):
def __init__(self, input_size, hidden_size, drop_p=0.2):
super(Feedforward, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.fc1 = torch.nn.Linear(self.input_size, self.hidden_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LavanayaBajaj/CREATE-CLASSIFIER | Feedforward | false | 774 | [
"MIT"
] | 0 | f00c7ec686f532a22e62d55aad169c831988be1b | https://github.com/LavanayaBajaj/CREATE-CLASSIFIER/tree/f00c7ec686f532a22e62d55aad169c831988be1b |
FixedSubnetConv | # 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
from torch import nn
import torch.nn.parallel
import torch.optim
imp... | MorganeAyle/SNIP-it | FixedSubnetConv | false | 858 | [
"MIT"
] | 0 | df2bf44d6d3f7e4ea7733242a79c916735a7b49e | https://github.com/MorganeAyle/SNIP-it/tree/df2bf44d6d3f7e4ea7733242a79c916735a7b49e |
MaskIOULoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | shunya-toyokawa/qanet_human_parts_segmentatiom | MaskIOULoss | false | 16,443 | [
"MIT"
] | 72 | 5527b247acd65534b455c26e3692a14b31669602 | https://github.com/shunya-toyokawa/qanet_human_parts_segmentatiom/tree/5527b247acd65534b455c26e3692a14b31669602 |
LanguageModelCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Maxi-0902/DRAN | LanguageModelCriterion | false | 827 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
NextImgPrediction | import torch
import torch.nn as nn
class NextImgPrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output size
"""
super().__init__()
self.linear = nn.Linear(hidden, 2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YanyuanQiao/HOP-VLN | NextImgPrediction | false | 18,130 | [
"MIT"
] | 8 | 4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | https://github.com/YanyuanQiao/HOP-VLN/tree/4b26b2569afb3e7eb7d8c2ed814cd424e41cbade |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return (waveform - waveform.mean()) / waveform.std()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | icyda17/very-deep-CNNs | Normalize | false | 10,218 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
UpsamplingBilinear | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | UpsamplingBilinear | false | 17,969 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 |
LearnedUtility | import torch
import torch.nn as nn
class LearnedUtility(nn.Module):
def __init__(self, slope=0):
super().__init__()
self.theta_tt = torch.nn.Parameter(slope * torch.ones(1))
self.theta_tt.requiresGrad = True
def forward(self, x):
return torch.multiply(self.theta_tt, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | pabloguarda/NeuralTransportationNetworks | LearnedUtility | false | 7,438 | [
"MIT"
] | 1 | 0461c26128b09488aff237b760068b43d131f8a9 | https://github.com/pabloguarda/NeuralTransportationNetworks/tree/0461c26128b09488aff237b760068b43d131f8a9 |
LossFunction | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class BPRLoss(nn.Module):
def __init__(self):
super(BPRLoss, self).__init__()
def forward(self, logit):
"""
Args:
logit (BxB): Variable that stores the logits for the it... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asser... | hungthanhpham94/GRU4REC-pytorch | LossFunction | false | 15,572 | [
"Apache-2.0"
] | 184 | 666b84264c4afae757fe55c6997dcf0a4da1d44e | https://github.com/hungthanhpham94/GRU4REC-pytorch/tree/666b84264c4afae757fe55c6997dcf0a4da1d44e |
ChannelSpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | YilinLiu97/AmygNet-Pytorch | ChannelSpatialSELayer3D | false | 18,147 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
InputInjection | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AnonSubmission6150/submission6150 | InputInjection | false | 8,975 | [
"Apache-2.0"
] | 0 | 571633d9a12b4fd7a9546947787fc068966dab04 | https://github.com/AnonSubmission6150/submission6150/tree/571633d9a12b4fd7a9546947787fc068966dab04 |
RMulFloat | import torch
class RMulFloat(torch.nn.Module):
def __init__(self):
super(RMulFloat, self).__init__()
def forward(self, x):
return 10.0 * 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | RMulFloat | false | 2,553 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | salem-devloper/Lung-Segmentation-Non-Covid | DiceLoss | false | 10,762 | [
"MIT"
] | 0 | 11eb87e46014aefaf034239bf68b65c5eb55711d | https://github.com/salem-devloper/Lung-Segmentation-Non-Covid/tree/11eb87e46014aefaf034239bf68b65c5eb55711d |
Hsigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guard... | EllisHui/outOfRailWay | Hsigmoid | false | 403 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
BoundedSingleVar | import torch
class BoundedSingleVar(torch.nn.Module):
"""Wrapper a single parameter to represent an unknown coefficient in inverse problem with the upper and lower bound.
:param lower_bound: The lower bound for the parameter.
:type lower_bound: float
:param upper_bound: The upper bound for the parame... | 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... | zweien/idrlnet | BoundedSingleVar | false | 16,833 | [
"Apache-2.0"
] | 66 | 3a19a3301d565c0906aac84ff31eefcff75726a8 | https://github.com/zweien/idrlnet/tree/3a19a3301d565c0906aac84ff31eefcff75726a8 |
Scale_and_shift | import torch
import torch.nn as nn
class Scale_and_shift(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.rand(1))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
return self.weight * x + self.bias
def get_inputs():
retu... | 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... | BCV-Uniandes/SAMA | Scale_and_shift | false | 128 | [
"BSD-3-Clause"
] | 0 | 4c732c71486af17efed17480e363298cb65c851f | https://github.com/BCV-Uniandes/SAMA/tree/4c732c71486af17efed17480e363298cb65c851f |
FFNLogReg | import torch
import torch.nn as nn
class FFN(nn.Module):
"""Feed Forward Network."""
def __init__(self, num_features: 'int', ffn_dim_1: 'int', ffn_dim_2: 'int'
) ->None:
"""Initialize the class."""
super().__init__()
self.gemm1 = nn.Linear(num_features, ffn_dim_1, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BruceRayWilson/sambanova_starter | FFNLogReg | false | 8,905 | [
"MIT"
] | 0 | be1b01369b040d00f174a0ee1fdb22e89ef40062 | https://github.com/BruceRayWilson/sambanova_starter/tree/be1b01369b040d00f174a0ee1fdb22e89ef40062 |
EdgeCaseModel | # 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... | EarthLab-Luxembourg/torch-summary | EdgeCaseModel | false | 412 | [
"MIT"
] | 0 | 8ef25aea5e9fb075df27e1e0c77bad56a7254397 | https://github.com/EarthLab-Luxembourg/torch-summary/tree/8ef25aea5e9fb075df27e1e0c77bad56a7254397 |
CompositePrior | import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
def swish(x):
return x.mul(torch.sigmoid(x))
def log_norm_pdf(x, mu, logvar):
return -0.5 * (logvar + np.log(2 * np.pi) + (x - mu).pow(2) / logvar.exp())
class Encoder(nn.Module):
def __init__(self, hidden_dim, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | verachtertr/RecVAE | CompositePrior | false | 13,099 | [
"Apache-2.0"
] | 0 | 915bed7f537cac6fc918aac8c622112561d15f03 | https://github.com/verachtertr/RecVAE/tree/915bed7f537cac6fc918aac8c622112561d15f03 |
DuelingQnet | # 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 random
import torch.nn... | DeepHaeJoong/reinforcement-learning | DuelingQnet | false | 9,033 | [
"MIT"
] | 0 | 63e3053e3209809e67e97d51adaf5f85ce3799ba | https://github.com/DeepHaeJoong/reinforcement-learning/tree/63e3053e3209809e67e97d51adaf5f85ce3799ba |
GLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | BedirYilmaz/picturate-mwml | GLU | false | 2,017 | [
"MIT"
] | 0 | e0dd1bb9df0e0ee5a9cbefba9ac7ada19a2cc41c | https://github.com/BedirYilmaz/picturate-mwml/tree/e0dd1bb9df0e0ee5a9cbefba9ac7ada19a2cc41c |
LocationLayer | # 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
from torch import nn
assert_size_stride = torch._C._dyna... | AeroXi/Tacotron2-Mandarin | LocationLayer | false | 13,300 | [
"MIT"
] | 67 | b7bc213d1c1a9c3e2f2e11f69f586c2582010668 | https://github.com/AeroXi/Tacotron2-Mandarin/tree/b7bc213d1c1a9c3e2f2e11f69f586c2582010668 |
BinaryLogisticRegressionLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SvipRepetitionCounting/TransRAC | BinaryLogisticRegressionLoss | false | 5,867 | [
"Apache-2.0"
] | 1 | eec12553dfa1e2fde6356b0e2703c633d225feb3 | https://github.com/SvipRepetitionCounting/TransRAC/tree/eec12553dfa1e2fde6356b0e2703c633d225feb3 |
DisAlignCosineFastRCNNOutputLayers | # 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.... | tonysy/cvpods | DisAlignCosineFastRCNNOutputLayers | false | 16,615 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
CausalConv1d | import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | gaetangate/FewRel | CausalConv1d | false | 12,405 | [
"MIT"
] | 0 | 150199d1060571315b1f370b3b3352d7a7c72dd5 | https://github.com/gaetangate/FewRel/tree/150199d1060571315b1f370b3b3352d7a7c72dd5 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:param hidden_dim:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HeGuanyuan/ABSA-PyTorch | Attention | false | 2,352 | [
"MIT"
] | 0 | 8244aeb39007a2714ccbfd54629ddbbb013ea87e | https://github.com/HeGuanyuan/ABSA-PyTorch/tree/8244aeb39007a2714ccbfd54629ddbbb013ea87e |
DNNModel | # 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... | ehsangolshani/workload-to-metric-mapper | DNNModel | false | 12,341 | [
"Apache-2.0"
] | 0 | 4c2825696200748382247909f2f777f49bf62cf0 | https://github.com/ehsangolshani/workload-to-metric-mapper/tree/4c2825696200748382247909f2f777f49bf62cf0 |
SqueezeEmbedding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | froth-synthesio/PyABSA | SqueezeEmbedding | false | 15,367 | [
"MIT"
] | 199 | 61406e7a49f93f6c986dfd7e583d730b69c2861c | https://github.com/froth-synthesio/PyABSA/tree/61406e7a49f93f6c986dfd7e583d730b69c2861c |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1)
self.conv2 = nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv2(F.relu(self.conv1(x)))
def get_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AutuanLiu/PyTorch-ML | Net | false | 16,971 | [
"MIT"
] | 9 | 884c7723843d9ffb4da09d95eb97886b2cc38f28 | https://github.com/AutuanLiu/PyTorch-ML/tree/884c7723843d9ffb4da09d95eb97886b2cc38f28 |
evolution_area | # 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... | LiWentomng/boxlevelset | evolution_area | false | 8,462 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
InstanceNorm | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class InstanceNorm(Module):
"""
## Instance Normalization Layer
Instance normalization layer $\\text{IN}$ normalizes the input $X$ as follows:
When input $X \\in \\m... | 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... | techthiyanes/annotated_deep_learning_paper_implementations | InstanceNorm | false | 16,561 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
MobileNetV3Classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
DisparityRegression | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | SpadeLiu/Graft-PSMNet | DisparityRegression | false | 1,094 | [
"MIT"
] | 0 | 1f2950d5afd85237f8d3604caab20dd47a8c9889 | https://github.com/SpadeLiu/Graft-PSMNet/tree/1f2950d5afd85237f8d3604caab20dd47a8c9889 |
MAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Behrouz-Babaki/NCG4CVRP | MAB | false | 4,901 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
MemoryUpdater | # 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.... | IsmaelElsharkawi/new_pororo_repo | MemoryUpdater | false | 8,821 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio | PixelNorm | false | 890 | [
"MIT"
] | 0 | 231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 | https://github.com/NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio/tree/231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 |
EqualLinear | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
"""Rescale weights after every updates.
"""
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
wei... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 math import sqrt
assert_size_stride = torch._C._dynamo... | hologerry/style-based-gan-pytorch | EqualLinear | false | 3,621 | [
"MIT"
] | 0 | 1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 | https://github.com/hologerry/style-based-gan-pytorch/tree/1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 |
LogSumExpPool | # 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... | alinstein/X_RAY | LogSumExpPool | false | 18,259 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
biaffine_mapping | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class biaffine_mapping(nn.Module):
def __init__(self, input_size_x, input_size_y, output_size, bias_x,
bias_y, initializer=None):
super(biaffine_mapping, self).__init__()
self.bias_x = bias_x
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
import torch.utils.data.dataloader
import torch.nn
assert_... | ciaochiaociao/CLNER | biaffine_mapping | false | 3,425 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
GlobalAveragePool | import torch
from torch import nn
import torch.onnx
class GlobalAveragePool(nn.Module):
def forward(self, input: 'torch.Tensor'):
spatial_shape = input.ndimension() - 2
dim = tuple(range(spatial_shape, spatial_shape + 2))
return torch.mean(input, dim=dim, keepdim=True)
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 import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | jiuntian/onnx2pytorch | GlobalAveragePool | false | 10,294 | [
"Apache-2.0"
] | 0 | fadca10a6045f4373293c9c0854607fb51a47c12 | https://github.com/jiuntian/onnx2pytorch/tree/fadca10a6045f4373293c9c0854607fb51a47c12 |
TorchFloorDiv | # 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... | PogChamper/torch2trt | TorchFloorDiv | false | 14,227 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
RNNModel | # 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
import ... | YuXie96/time | RNNModel | false | 1,279 | [
"MIT"
] | 0 | 8539d55d2449c712f54331b06720ab7faf3593df | https://github.com/YuXie96/time/tree/8539d55d2449c712f54331b06720ab7faf3593df |
Actor | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, 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.... | DiegelD/Deep-Reinforcement-Learning-ND | Actor | false | 11,352 | [
"MIT"
] | 0 | 15a91da352414718bb83fdc538d73ac576472cb8 | https://github.com/DiegelD/Deep-Reinforcement-Learning-ND/tree/15a91da352414718bb83fdc538d73ac576472cb8 |
DownConv | import copy
import torch
import torch.nn as nn
def get_conv(dim=3):
"""Chooses an implementation for a convolution layer."""
if dim == 3:
return nn.Conv3d
elif dim == 2:
return nn.Conv2d
else:
raise ValueError('dim has to be 2 or 3')
def planar_kernel(x):
"""Returns a "pl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 copy
import torch.nn a... | PlayWeird/ct-volume-preprocessing | DownConv | false | 5,720 | [
"MIT"
] | 1 | 8bacf58c36c001fcdb809d4f74e9a39acb00bcbe | https://github.com/PlayWeird/ct-volume-preprocessing/tree/8bacf58c36c001fcdb809d4f74e9a39acb00bcbe |
Neural_Net | import torch
import torch.utils.data
import torch.nn as nn
class Neural_Net(nn.Module):
def __init__(self, D_in):
"""
Neural Network model with 1 hidden layer.
D_in: Dimension of input
"""
super(Neural_Net, self).__init__()
self.fc1 = nn.Linear(D_in, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | Bradnowak/Flowlytic | Neural_Net | false | 184 | [
"BSD-2-Clause"
] | 0 | d5b0951901b97d5546f2ecd700eee5b78c689646 | https://github.com/Bradnowak/Flowlytic/tree/d5b0951901b97d5546f2ecd700eee5b78c689646 |
fullyCon | # 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_... | Lightingooo/- | fullyCon | false | 5,796 | [
"MIT"
] | 1 | 7b48c2689b693617e46992ac081065cf08f14bf8 | https://github.com/Lightingooo/-/tree/7b48c2689b693617e46992ac081065cf08f14bf8 |
ContrastiveLoss | # 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.optim
from typing import NoReturn
import torch
import torch.nn as nn
assert_... | DengBoCong/text-sim | ContrastiveLoss | false | 7,945 | [
"MIT"
] | 21 | 2c6c323649aa259a7b3d5c6d3714bd1860114826 | https://github.com/DengBoCong/text-sim/tree/2c6c323649aa259a7b3d5c6d3714bd1860114826 |
DotProductSimilarity | import math
import torch
import torch.nn as nn
class SimilarityFunction(nn.Module):
"""
A ``SimilarityFunction`` takes a pair of tensors with the same shape, and computes a similarity
function on the vectors in the last dimension. For example, the tensors might both have shape
`(batch_size, sentence_... | 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... | Aunsiels/qagnn | DotProductSimilarity | false | 11,299 | [
"MIT"
] | 0 | d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 | https://github.com/Aunsiels/qagnn/tree/d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 |
CecaModule | # 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.parallel
import torch._utils
i... | Alicegaz/torchok | CecaModule | false | 16,929 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
ReviewClassifier | # 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... | AnissHal/tal | ReviewClassifier | false | 1,949 | [
"MIT"
] | 0 | 6e96ffa367be6da54383ae9e6b0f56f7b5cf9a92 | https://github.com/AnissHal/tal/tree/6e96ffa367be6da54383ae9e6b0f56f7b5cf9a92 |
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 import nn
assert_s... | TommeyChang/CS294-Homework | Policy | false | 1,143 | [
"MIT"
] | 0 | 17b525bf4366034b45c4febd89f1053d44550237 | https://github.com/TommeyChang/CS294-Homework/tree/17b525bf4366034b45c4febd89f1053d44550237 |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Crawford-fang/ROS_pytorch_RL | QNetwork | false | 17,165 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_unit(layer):
inp = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(inp)
return -lim, lim
class Critic(nn.Module):
def __init__(self, state_size, action_size, seed=2, fc1_units=256,
fc2_units=256... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | bnriiitb/Deep-Reinforcement-Learning | Critic | false | 6,358 | [
"MIT"
] | 1 | 5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e | https://github.com/bnriiitb/Deep-Reinforcement-Learning/tree/5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e |
PolicyModuleAlt | # 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_... | bentrevett/task-oriented-language-grounding | PolicyModuleAlt | false | 6,319 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
LearnablePositionalEncoding | import torch
import torch.nn as nn
class LearnablePositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=1024):
super(LearnablePositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
self.pe = nn.Parameter(torch.empty(max_len, 1, d_model))
... | 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... | gaowanting/paper_code0 | LearnablePositionalEncoding | false | 3,523 | [
"MIT"
] | 0 | 15568fc9989b26df7c582b92163d2f262654712e | https://github.com/gaowanting/paper_code0/tree/15568fc9989b26df7c582b92163d2f262654712e |
Word2Vec | # 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.... | abhinavbh08/NNTI-WS2021-NLP-Project | Word2Vec | false | 9,658 | [
"MIT"
] | 0 | 946cfdcb0e0e64969d12423fa1b26dad3cb2d417 | https://github.com/abhinavbh08/NNTI-WS2021-NLP-Project/tree/946cfdcb0e0e64969d12423fa1b26dad3cb2d417 |
InnerProductLayer | # 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... | Ahren09/RecBole | InnerProductLayer | false | 1,924 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
Similarity | # 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.... | carol-hsu/relay-bench | Similarity | false | 3,272 | [
"Apache-2.0"
] | 0 | 0facffedb3cbb0d5f110769a84bba68718cff72b | https://github.com/carol-hsu/relay-bench/tree/0facffedb3cbb0d5f110769a84bba68718cff72b |
SSIM | import torch
import torch.nn as nn
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
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(... | 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
... | Siddharth-Shrivastava7/DANNet | SSIM | false | 14,418 | [
"Apache-2.0"
] | 61 | 8db10056a4e445d24fc899505923615457cae5b7 | https://github.com/Siddharth-Shrivastava7/DANNet/tree/8db10056a4e445d24fc899505923615457cae5b7 |
CFRB | # 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
from col... | samuro95/Prox-PnP | CFRB | false | 10,993 | [
"MIT"
] | 0 | c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 | https://github.com/samuro95/Prox-PnP/tree/c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 |
SimpleFloorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFloorModule(torch.nn.Module):
def forward(self, a, b):
c = a + b
return torch.floor(c)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleFloorModule | false | 3,424 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
UpscaleBlock | import torch
import torch.nn as nn
class UpscaleBlock(nn.Module):
""" Upscaling Block using Pixel Shuffle to increase image dimensions. Used in Generator Network"""
"""
Pixel shuffle layer
(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
Neural Network,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | grofit/traiNNer | UpscaleBlock | false | 15,479 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | RicCu/NeuralStyle | TVLoss | false | 5,783 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
DC | import torch
from torch import nn
import torch.nn.functional
class DC(nn.Module):
def __init__(self, nb_classes):
super(DC, self).__init__()
self.softmax = nn.Softmax(1)
self.nb_classes = nb_classes
@staticmethod
def onehot(gt, shape):
gt = gt.long()
y_onehot = to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | ReubenDo/InExtremIS | DC | false | 8,693 | [
"MIT"
] | 17 | 1512ddf9b8c11c4d9f0ebd465d904ef3d539d350 | https://github.com/ReubenDo/InExtremIS/tree/1512ddf9b8c11c4d9f0ebd465d904ef3d539d350 |
InstanceNorm | # 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_... | Holmes-Alan/Photo2Sketch | InstanceNorm | false | 529 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
EnergyConservingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EnergyConservingLoss(nn.L1Loss):
"""Energy conserving loss.
A two term loss that enforces energy conservation after
:cite:`Rethage2018`.
The loss can be described as:
.. math::
\\ell(x, y, m) = L = \\{l_1,\\dots,l_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | hagenw/audtorch | EnergyConservingLoss | false | 15,482 | [
"MIT"
] | 81 | d82ae7f7f8c7edb7b7180b83442224e9a68483bd | https://github.com/hagenw/audtorch/tree/d82ae7f7f8c7edb7b7180b83442224e9a68483bd |
MultiHeadedAttention | import math
import torch
import torch.nn.functional as F
class MultiHeadedAttention(torch.nn.Module):
"""
Implement of multi-head attention.
"""
def __init__(self, n_heads, hidden_size, drop_rate):
super().__init__()
assert hidden_size % n_heads == 0
self.n_dk = 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.... | haophancs/TREQS | MultiHeadedAttention | false | 15,501 | [
"MIT"
] | 149 | 49e354ce2a08cf963ec139d99936020e0f80ced8 | https://github.com/haophancs/TREQS/tree/49e354ce2a08cf963ec139d99936020e0f80ced8 |
MinMaxNormalizer | import torch
def min_max_normalizer(x, detach=False):
x_min = torch.min(x)
x_max = torch.max(x)
if detach:
x_min = x_min.detach()
x_max = x_max.detach()
return (x - x_min) / (x_max - x_min)
class MinMaxNormalizer(torch.nn.Module):
def __init__(self, detach=False):
super(... | 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 | MinMaxNormalizer | false | 13,951 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
InstrShifting | # 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
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | IMNearth/Curriculum-Learning-For-VLN | InstrShifting | false | 17,437 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 |
SpatialGather_Module | # 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.... | NikV-JS/semantic-segmentation | SpatialGather_Module | false | 11,765 | [
"BSD-3-Clause"
] | 0 | 68fd9ddc5498590371f064c8bebb03ac80932766 | https://github.com/NikV-JS/semantic-segmentation/tree/68fd9ddc5498590371f064c8bebb03ac80932766 |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim, ... | 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... | FrederikKromann/dtu_mlops | Encoder | false | 481 | [
"Apache-2.0"
] | 0 | b82e43e1a1b58f0ba208414092e4c0ea63c5d4ff | https://github.com/FrederikKromann/dtu_mlops/tree/b82e43e1a1b58f0ba208414092e4c0ea63c5d4ff |
MSELoss | # 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 functools
from torch.nn import functional as F
import torch.nn as nn
import torch.... | Atten4Vis/DemystifyLocalViT | MSELoss | false | 13,338 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | CarnoZhao/mmsegmentation | Block | false | 7,855 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
GraphConvolution | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
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.nn import Module
import torch.autograd
import torch.nn as nn
from tor... | SowmyaAitha/Palmira | GraphConvolution | false | 17,940 | [
"MIT"
] | 6 | c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | https://github.com/SowmyaAitha/Palmira/tree/c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad |
SimpleMinModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | opti-mix/glow | SimpleMinModule | false | 7,409 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Highway | import torch
from torch import nn
class Highway(nn.Module):
"""
Individual highway layer
"""
def __init__(self, input_dim, activation_class=nn.ReLU):
"""
Create a highway layer. The input is a tensor of features, the output
is a tensor with the same dimension.
With in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | abhinonymous/MSMARCO-Question-Answering | Highway | false | 14,746 | [
"MIT"
] | 127 | bfdd802d20b63322adca23f1da1f6a5931593920 | https://github.com/abhinonymous/MSMARCO-Question-Answering/tree/bfdd802d20b63322adca23f1da1f6a5931593920 |
MultinomialNLLLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | akshayka/gavel | MultinomialNLLLoss | false | 14,795 | [
"MIT"
] | 67 | 40a22a725f2e70478483e98c9b07c6fc588e0c40 | https://github.com/akshayka/gavel/tree/40a22a725f2e70478483e98c9b07c6fc588e0c40 |
spatial_attn_layer | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, 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
import torch.nn as nn
import ... | zhanzhibingshang/deblurganv2_mirnet | spatial_attn_layer | false | 11,045 | [
"BSD-3-Clause"
] | 0 | 12fcc94ee0ff33335c557cf46a776a13cae3804b | https://github.com/zhanzhibingshang/deblurganv2_mirnet/tree/12fcc94ee0ff33335c557cf46a776a13cae3804b |
HardSigmoid | import torch
import torch.nn.functional as F
class HardSigmoid(torch.nn.Module):
"""
Pytorch implementation of the hard sigmoid activation function
"""
def __init__(self):
super(HardSigmoid, self).__init__()
def forward(self, input):
x = 0.2 * input + 0.5
x = torch.clamp(... | 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... | ShiraLightricks/3d-photo-inpainting | HardSigmoid | false | 1,064 | [
"MIT"
] | 0 | c42ac41576690b765e50f5281ddbfb58439ff36d | https://github.com/ShiraLightricks/3d-photo-inpainting/tree/c42ac41576690b765e50f5281ddbfb58439ff36d |
MarginDisparityDiscrepancy | # 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 typing import Opt... | NiteshBharadwaj/ignoringhumanpose | MarginDisparityDiscrepancy | false | 916 | [
"MIT"
] | 0 | 1fb7a063fded9cff18f7de4e1d71845983077256 | https://github.com/NiteshBharadwaj/ignoringhumanpose/tree/1fb7a063fded9cff18f7de4e1d71845983077256 |
MaxLayer | import torch
from torch import Tensor
import torch.nn
class MaxLayer(torch.nn.Module):
"""Placeholder Layer for Max operation"""
def __init__(self):
super(MaxLayer, self).__init__()
def forward(self, inputs: 'Tensor'):
return inputs.max(dim=-1)[0]
def get_inputs():
return [torch.ra... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | MoritzWag/LPDN | MaxLayer | false | 2,818 | [
"MIT"
] | 0 | a88a5a03f18c7f87879918369b8dc7a0e3abb02b | https://github.com/MoritzWag/LPDN/tree/a88a5a03f18c7f87879918369b8dc7a0e3abb02b |
Qux | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | Qux | false | 3,356 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
GaussianPolicy | # 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.... | JimmyMVP/plain_rl | GaussianPolicy | false | 17,487 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 |
LuongAttention | import torch
import torch.nn.functional as F
from torch import nn
class LuongAttention(nn.Module):
"""
Luong Attention from Effective Approaches to Attention-based Neural Machine Translation
https://arxiv.org/pdf/1508.04025.pdf
"""
def __init__(self, attention_dim):
super(LuongAttention, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | A-Jacobson/minimal-nmt | LuongAttention | false | 13,245 | [
"MIT"
] | 45 | dc75e83579a181586acabfa3f22ad269d1e31fbf | https://github.com/A-Jacobson/minimal-nmt/tree/dc75e83579a181586acabfa3f22ad269d1e31fbf |
vd_linear_1L | # 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.... | Neronjust2017/Bayesian-neural-networks | vd_linear_1L | false | 17,778 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | beneisner/pytorch_geometric | Attention | false | 6,328 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
BCEAfterSigmoidLoss | # 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 ... | johnbachman/pykeen | BCEAfterSigmoidLoss | false | 3,761 | [
"MIT"
] | 0 | 6595f6cefc462b6d1e057446e6c3ed66d36a078b | https://github.com/johnbachman/pykeen/tree/6595f6cefc462b6d1e057446e6c3ed66d36a078b |
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