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 |
|---|---|---|---|---|---|---|---|---|---|---|
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch... | kangzhiq/DeepFillv2_Pytorch | LayerNorm | false | 10,429 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 |
RMSE_log | # 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... | Khoronus/MonoDepth-FPN-PyTorch | RMSE_log | false | 717 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | youqingxiaozhua/ABAW3 | MSELoss | false | 11,057 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(input):
return input * input.sigmoid()
class SE(nn.Module):
def __init__(self, in_channels, se_channels):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_channels, se_channels, kernel_size=1, bias=True
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CYHYCY/cifar10 | SE | false | 4,955 | [
"Apache-2.0"
] | 1 | 37254801045b76604a922884da87744aeb99b416 | https://github.com/CYHYCY/cifar10/tree/37254801045b76604a922884da87744aeb99b416 |
Model | import torch
class Model(torch.nn.Module):
def __init__(self, D_in, D_out):
super(Model, self).__init__()
self.w1 = torch.nn.Parameter(torch.randn(D_in, D_out),
requires_grad=True)
self.sig = torch.nn.Sigmoid()
def forward(self, x):
y_pred = self.sig(x.mm(self.w1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Saran-nns/gradients | Model | false | 1,018 | [
"MIT"
] | 0 | 67f9ff92589047828563dbbe30f225dca7ad47fd | https://github.com/Saran-nns/gradients/tree/67f9ff92589047828563dbbe30f225dca7ad47fd |
EmissionModel | import torch
import torch.utils.data
class EmissionModel(torch.nn.Module):
"""
- forward(): computes the log probability of an observation.
- sample(): given a state, sample an observation for that state.
"""
def __init__(self, N, M):
super(EmissionModel, self).__init__()
self.N = 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 math as tl_math
import torch.utils.dat... | dendisuhubdy/pytorch_HMM | EmissionModel | false | 15,156 | [
"Apache-2.0"
] | 88 | 3235326027328e1b0377b17f9dad8fcc56a3668c | https://github.com/dendisuhubdy/pytorch_HMM/tree/3235326027328e1b0377b17f9dad8fcc56a3668c |
BertSelfOutput | # 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 ... | igor0/BLIP | BertSelfOutput | false | 16,250 | [
"BSD-3-Clause"
] | 473 | 6d8c3f1e381ed23acb84c55b4adb80e74c08117a | https://github.com/igor0/BLIP/tree/6d8c3f1e381ed23acb84c55b4adb80e74c08117a |
SpatialAttention | # 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 tor... | dqawami/openvino_training_extensions | SpatialAttention | false | 15,218 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
AuxiliaryConvolutions | import torch
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
class AuxiliaryConvolutions(nn.Module):
"""
Additional convolutions to produce higher-level feature maps.
"""
def __init__(self):
super(Auxilia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ite... | dee-walia20/SSD-Implementation-using-Pytorch | AuxiliaryConvolutions | false | 6,644 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
Gate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size, dropout_rate=0.0):
super(Gate, self).__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
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... | Raiselimit/TorchBlocks | Gate | false | 5,751 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
CAT_TokenEmbedding | # 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... | mkmysk123456789/Informer2020 | CAT_TokenEmbedding | false | 7,250 | [
"Apache-2.0"
] | 1 | ad4b895169a17db580aab6d2c09fd07e06c9b6fa | https://github.com/mkmysk123456789/Informer2020/tree/ad4b895169a17db580aab6d2c09fd07e06c9b6fa |
InterProbCrossEntropyLoss | import torch
import torch.utils.data
class InterProbCrossEntropyLoss(torch.nn.Module):
def __init__(self, in_features, num_classes):
super(InterProbCrossEntropyLoss, self).__init__()
self.in_features = in_features
self.num_classes = num_classes
self.fc = torch.nn.Linear(in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tkc-morita/secl | InterProbCrossEntropyLoss | false | 10,951 | [
"MIT"
] | 0 | d0156cea4fd95ea5071126dbf076a6da69752a37 | https://github.com/tkc-morita/secl/tree/d0156cea4fd95ea5071126dbf076a6da69752a37 |
BerhuLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.hub
class BerhuLoss(nn.Module):
def __init__(self):
super(BerhuLoss, self).__init__()
self.name = 'Berhu'
def forward(self, input, target, mask=None):
assert input.shap... | 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
... | appliedinnovation/fast-depth | BerhuLoss | false | 1,458 | [
"MIT"
] | 0 | 4606b4d340ae416de94afed45bc767fe6f64bd67 | https://github.com/appliedinnovation/fast-depth/tree/4606b4d340ae416de94afed45bc767fe6f64bd67 |
AttentionBranch | # 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... | grofit/traiNNer | AttentionBranch | false | 15,461 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
Ln_distance | # 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
from torch import nn
import torch.utils.data
assert_size_strid... | carla-recourse/CARLA | Ln_distance | false | 15,000 | [
"MIT"
] | 140 | e9bb3152598a94e700c38d7377825054959dcf48 | https://github.com/carla-recourse/CARLA/tree/e9bb3152598a94e700c38d7377825054959dcf48 |
Contract | import torch
import torch.nn as nn
import torch.utils.data
class Contract(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, C, H // s, s, W // s, s)
x = x.permute... | 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.... | Arui66/FPSAutomaticAiming | Contract | false | 13,303 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
Hidden2Normal | import torch
class Hidden2Normal(torch.nn.Module):
def __init__(self, hidden_dim):
super(Hidden2Normal, self).__init__()
self.linear = torch.nn.Linear(hidden_dim, 5)
def forward(self, hidden_state):
normal = self.linear(hidden_state)
normal[:, 2] = 0.01 + 0.2 * torch.sigmoid(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories | Hidden2Normal | false | 17,512 | [
"MIT"
] | 9 | 488924e938fc1674b5a0d2cb9f05178cad8de561 | https://github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories/tree/488924e938fc1674b5a0d2cb9f05178cad8de561 |
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... | Charbel199/Oil-Spill-Thickness-Estimation | DiceLoss | false | 8,927 | [
"MIT"
] | 0 | dd600f6da611461f3b8072389bc34e6285109246 | https://github.com/Charbel199/Oil-Spill-Thickness-Estimation/tree/dd600f6da611461f3b8072389bc34e6285109246 |
FirstLSTMAmp | import torch
import torch.nn as nn
class FirstLSTMAmp(nn.Module):
"""
First LSTM amplifier branch.
Parameters:
----------
in_features : int
Number of input channels.
out_features : int
Number of output channels.
"""
def __init__(self, in_features, out_features):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | iofthetiger/pkuad | FirstLSTMAmp | false | 6,898 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Rehan-Ahmar/UNet-Zoo | DICELoss | false | 14,300 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
Aggregation | import torch
from torch import nn
from torch.nn import *
class Aggregation(nn.Module):
"""
Aggregation layer for the Dueling architecture.
https://arxiv.org/abs/1511.06581
This layer computes a Q function by combining
an estimate of V with an estimate of the advantage.
The advantage is normal... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._d... | kcorder/autonomous-learning-library | Aggregation | false | 15,796 | [
"MIT"
] | 584 | 0266195fa47564e51a32087bc007bff6dda5e263 | https://github.com/kcorder/autonomous-learning-library/tree/0266195fa47564e51a32087bc007bff6dda5e263 |
DuelingQnet | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class DuelingQnet(nn.Module):
def __init__(self, actions=2):
super(DuelingQnet, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc_value = nn.Linear(128, 128)
self.fc_adv = nn.Linear(128, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
Adder2D | from torch.autograd import Function
import math
import torch
import torch.nn as nn
from torch.autograd.function import Function
def adder2d_function(X, W, stride=1, padding=0):
n_filters, _d_filter, h_filter, w_filter = W.size()
n_x, _d_x, h_x, w_x = X.size()
h_out = (h_x - h_filter + 2 * padding) / strid... | 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.autograd import Function
import math
import torch.nn as nn
fro... | poppin-mice/ShiftAddNet | Adder2D | false | 16,268 | [
"MIT"
] | 55 | a17369a50da5bba6250fdeac7c065bd00f293f3c | https://github.com/poppin-mice/ShiftAddNet/tree/a17369a50da5bba6250fdeac7c065bd00f293f3c |
DiceCoeffLoss | # 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... | ZiyunClaudeWang/e3d | DiceCoeffLoss | false | 18,224 | [
"MIT"
] | 9 | 2efd01167350c29423babb6233907fa54156268f | https://github.com/ZiyunClaudeWang/e3d/tree/2efd01167350c29423babb6233907fa54156268f |
IIDIsotropicGaussianUVLoss | # 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 math... | AbirKhan96/facebook-detectron2 | IIDIsotropicGaussianUVLoss | false | 16,861 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 |
SelfAttentionPooling | # 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.... | sumanthd17/s3prl | SelfAttentionPooling | false | 12,999 | [
"MIT"
] | 0 | bb74c705295d121c4308ceb6b6a2c8d1814d6f4c | https://github.com/sumanthd17/s3prl/tree/bb74c705295d121c4308ceb6b6a2c8d1814d6f4c |
ManifoldPropagation | import torch
import torch.nn as nn
import torch.nn.functional as F
def shift(x, direction, amount):
if direction == 'left':
ret = F.pad(x, (amount, 0, 0, 0, 0, 0, 0, 0))[:, :, :, :-amount]
elif direction == 'right':
ret = F.pad(x, (0, amount, 0, 0, 0, 0, 0, 0))[:, :, :, amount:]
elif direc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | wonkyunglee/MPNet | ManifoldPropagation | false | 16,758 | [
"MIT"
] | 1,280 | 3a6821a88a5e3db5bd97121761dbb361d9518bc2 | https://github.com/wonkyunglee/MPNet/tree/3a6821a88a5e3db5bd97121761dbb361d9518bc2 |
L2loss | # 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... | btolooshams/densae | L2loss | false | 6,365 | [
"MIT"
] | 1 | a1e4c4cc1b4be0386d42136f2695615ea3cf4815 | https://github.com/btolooshams/densae/tree/a1e4c4cc1b4be0386d42136f2695615ea3cf4815 |
Mlp | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
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.triton_helpers import libdevice
import torch.nn as ... | ChangeTheWorld20191008/SwinIR | Mlp | false | 11,293 | [
"Apache-2.0"
] | 0 | a0cf7330b10e7c7294f11f59e1b89eff973b9093 | https://github.com/ChangeTheWorld20191008/SwinIR/tree/a0cf7330b10e7c7294f11f59e1b89eff973b9093 |
MaxPool1d | import torch
import torch.nn as nn
class MaxPool1d(nn.Module):
def __init__(self, win=2, stride=None, pad=0):
super().__init__()
self.pooling = nn.MaxPool1d(kernel_size=win, stride=stride, padding=pad
)
def forward(self, x):
"""
Args:
x: shape=(batch_s... | 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... | WiseDoge/Text-Classification-PyTorch | MaxPool1d | false | 18,091 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 |
SmoothL1Loss | # 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
... | ZephyrII/mmpose_charger | SmoothL1Loss | false | 12,012 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
resnet_block | import torch
import torch.nn as nn
import torch.nn.functional as F
class resnet_block(nn.Module):
def __init__(self, dim_in, dim_out):
super(resnet_block, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
if self.dim_in == self.dim_out:
self.conv_1 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | luixiao1223/BSP-NET-pytorch | resnet_block | false | 3,940 | [
"MIT"
] | 0 | f871c8ce6a9d52ac922e110702c47cd1c89d0a73 | https://github.com/luixiao1223/BSP-NET-pytorch/tree/f871c8ce6a9d52ac922e110702c47cd1c89d0a73 |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
"""
Creates a criterion that computes the Intersection over Union (IoU)
between a segmentation mask and its ground truth.
Rahman, M.A. and Wang, Y:
Optimizing Intersection-Over-Union in Deep Neural Networ... | 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... | BCV-Uniandes/DMS | IoULoss | false | 13,350 | [
"MIT"
] | 66 | 9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 | https://github.com/BCV-Uniandes/DMS/tree/9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 |
ContextgenCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContextgenCNN(nn.Module):
def __init__(self, latent_dim=1024):
super(ContextgenCNN, self).__init__()
self.latent_dim = latent_dim
self.conv1 = nn.Conv2d(self.latent_dim, self.latent_dim // 4, 1,
stride=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 ... | SarodYatawatta/federated-pytorch-test | ContextgenCNN | false | 8,765 | [
"Apache-2.0"
] | 33 | 42a51ba12a92b32fa19273340d5b61e74e11d8e0 | https://github.com/SarodYatawatta/federated-pytorch-test/tree/42a51ba12a92b32fa19273340d5b61e74e11d8e0 |
FCLayer | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | StevenChaoo/R-BERT-DDI | FCLayer | false | 1,091 | [
"MIT"
] | 0 | 6d9666e0bc61397ca942ffad53653690c1e8a899 | https://github.com/StevenChaoo/R-BERT-DDI/tree/6d9666e0bc61397ca942ffad53653690c1e8a899 |
CumulativeLinkLoss | import torch
import numpy as np
from torch import nn
from typing import Optional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def _reduction(loss: 'torch.Tensor', reduction: 'str') ->torch.Tensor:
"""
Reduce loss
Parameters
----------
loss... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
fro... | Ramstein/Retinopathy2 | CumulativeLinkLoss | false | 972 | [
"MIT"
] | 0 | 669e74206c466e6351d4e3df6087c6aa39b5c6c2 | https://github.com/Ramstein/Retinopathy2/tree/669e74206c466e6351d4e3df6087c6aa39b5c6c2 |
SplitAndConcat | # 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.... | tsubauaaa/d2go | SplitAndConcat | false | 4,461 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
TensorExp | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Minyus/pipelinex | TensorExp | false | 14,033 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
RpowInt | import torch
class RpowInt(torch.nn.Module):
def __init__(self):
super(RpowInt, self).__init__()
def forward(self, x):
return 2 ** 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._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | RpowInt | false | 2,541 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
SDPAttention | # 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.... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | SDPAttention | false | 3,775 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
CossimLoss | import torch
import torch.nn as nn
class CossimLoss(nn.Module):
def __init__(self, dim: 'int'=1, eps: 'float'=1e-08):
super().__init__()
self.cos_sim = nn.CosineSimilarity(dim, eps)
def forward(self, output, target):
return -self.cos_sim(output, target).mean() + 1
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 libdevice
import torch.nn as nn
assert... | Geson-anko/VQ_AutoEncoder | CossimLoss | false | 2,278 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
EuclideanLoss | # 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... | IndigoPurple/EFENet | EuclideanLoss | false | 8,290 | [
"MIT"
] | 11 | e88234486f19534274a0a20badc251788ac67e31 | https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31 |
nin | # 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 ... | elahekhodaie/PixelCnnPP | nin | false | 10,070 | [
"MIT"
] | 0 | ab1e245ed8c24009364b1f891288eb1a526b0121 | https://github.com/elahekhodaie/PixelCnnPP/tree/ab1e245ed8c24009364b1f891288eb1a526b0121 |
ActorNet | # 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_... | Kanaderu/spiking-ddpg-mapless-navigation | ActorNet | false | 8,384 | [
"MIT"
] | 29 | 2b5e7e67385dee4428b8036bc4ffe95e812b34e0 | https://github.com/Kanaderu/spiking-ddpg-mapless-navigation/tree/2b5e7e67385dee4428b8036bc4ffe95e812b34e0 |
LT | import torch
class LT(torch.nn.Module):
def __init__(self):
super(LT, self).__init__()
def forward(self, x, y):
return x < y
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | bunderhi/torch2trt | LT | false | 1,600 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | zhangxinaaaa/Conv-TasNet | Encoder | false | 11,037 | [
"MIT"
] | 0 | 4622d93d0b9dbe23584addd4f4b9463255651652 | https://github.com/zhangxinaaaa/Conv-TasNet/tree/4622d93d0b9dbe23584addd4f4b9463255651652 |
UFOAttention | # 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... | LiChengChen666/DetectDee | UFOAttention | false | 9,835 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
ClippedLinearQuantization | import torch
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:
input.add_(zero_p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.optim.lr_schedule... | HatsuneMiku4/distiller | ClippedLinearQuantization | false | 2,390 | [
"Apache-2.0"
] | 0 | 8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 | https://github.com/HatsuneMiku4/distiller/tree/8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 |
PrototypicalNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_st... | Basasuya/FewShotWithoutForgetting | PrototypicalNetwork | false | 3,469 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
EncoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class FeedF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | matatabinoneko/densecap | EncoderLayer | false | 12,776 | [
"BSD-3-Clause"
] | 0 | 723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd | https://github.com/matatabinoneko/densecap/tree/723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd |
first_conv | import torch
import torch.nn as nn
import torch.nn.functional as F
class first_conv(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=False):
super(first_conv, self).__init__(in_channels, out_channels,
kernel_size, s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RuiLin0212/BATMANN | first_conv | false | 17,854 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
ActorCritic | # 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... | tpbarron/pytorch-ppo | ActorCritic | false | 16,617 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
LayerNormGRUCell | import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.nn
from torch.nn import RNNCellBase
import torch.multiprocessing
from torch.nn import Identity
class LayerNormGRUCell(RNNCellBase):
"""
Implements GRUCell with layer normalisation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | faz1993/InnerEye-DeepLearning | LayerNormGRUCell | false | 15,339 | [
"MIT"
] | 402 | fb258d5c9a3ba18565b5a67e7ac1f00127d9ecb9 | https://github.com/faz1993/InnerEye-DeepLearning/tree/fb258d5c9a3ba18565b5a67e7ac1f00127d9ecb9 |
StatsNet | # 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_... | angelicagardner/ensemble-cnn-deepfakes-detection | StatsNet | false | 1,438 | [
"BSD-3-Clause"
] | 0 | 8740d2317848250249c741e0af5c4cbbe2d8af46 | https://github.com/angelicagardner/ensemble-cnn-deepfakes-detection/tree/8740d2317848250249c741e0af5c4cbbe2d8af46 |
Downsample2d | # 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
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_s... | ai-in-motion/moai | Downsample2d | false | 18,312 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
RDivInt | import torch
class RDivInt(torch.nn.Module):
def __init__(self):
super(RDivInt, self).__init__()
def forward(self, x):
return 100 / 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... | PogChamper/torch2trt | RDivInt | false | 14,216 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
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_... | Arjung27/DeepThinking | MLP | false | 16,942 | [
"MIT"
] | 6 | 13a2ce534bcb0b9379a22fffef52d975d650adb2 | https://github.com/Arjung27/DeepThinking/tree/13a2ce534bcb0b9379a22fffef52d975d650adb2 |
SReLU | # 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 import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | SheffieldAI/pykale | SReLU | false | 14,399 | [
"MIT"
] | 324 | be7670941fb06835883c80477b26702d407017db | https://github.com/SheffieldAI/pykale/tree/be7670941fb06835883c80477b26702d407017db |
SoftIoU | import torch
import torch.nn as nn
import torch._utils
class SoftIoU(nn.Module):
def __init__(self, from_sigmoid=False, ignore_label=-1):
super().__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
def forward(self, pred, label):
label = label.view(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._dynamo.guards.as... | aagaard/ritm_interactive_segmentation | SoftIoU | false | 12,034 | [
"MIT"
] | 0 | c68b45a54e99eb5401f50e62f7e43a11e34964ee | https://github.com/aagaard/ritm_interactive_segmentation/tree/c68b45a54e99eb5401f50e62f7e43a11e34964ee |
BinaryClassificationHead | from _paritybench_helpers import _mock_config
import torch
class BinaryClassificationHead(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = torch.nn.Dropout(conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BunnyNoBugs/DeepPavlov | BinaryClassificationHead | false | 12,322 | [
"Apache-2.0"
] | 0 | b2213db633a669d27d6f745dd780530574ccf8b5 | https://github.com/BunnyNoBugs/DeepPavlov/tree/b2213db633a669d27d6f745dd780530574ccf8b5 |
Gather | # 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.... | jhp038/fashion_project | Gather | false | 3,718 | [
"MIT"
] | 0 | 719533dc60155801f567e6a9183d7a5036ee1166 | https://github.com/jhp038/fashion_project/tree/719533dc60155801f567e6a9183d7a5036ee1166 |
FM | # 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 sklearn.metrics import *
import torch.onnx
import torch as torch
assert_size_stride = torch._C._dynamo.guards.ass... | dulvqingyunLT/DeepCTR-Torch | FM | false | 10,317 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc |
DeConv2d | import functools
import torch
from torch import nn
from typing import Optional
import torch.nn.functional as F
class DeConv2d(nn.Conv2d):
def __init__(self, in_nc: 'int', out_nc: 'int', kernel_size: 'int',
scale: 'int'=2, mode: 'str'='nearest', align_corners:
'Optional[bool]'=None, **kwargs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 functools
from torch import nn
from typing import Optional
import torch.n... | pomelyu/ML_HW | DeConv2d | false | 10,711 | [
"MIT"
] | 0 | b87697f3ee86592a34d80c8dbf167a5767731630 | https://github.com/pomelyu/ML_HW/tree/b87697f3ee86592a34d80c8dbf167a5767731630 |
RDivFloat | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | RDivFloat | false | 2,539 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
PCC | import torch
import torch.nn.functional
class PCC(torch.nn.Module):
def __init__(self):
super(PCC, self).__init__()
def pcc(self, y_true, y_pred):
A_bar = torch.mean(y_pred, dim=[1, 2, 3, 4], keepdim=True)
B_bar = torch.mean(y_true, dim=[1, 2, 3, 4], keepdim=True)
top = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional
a... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | PCC | false | 15,760 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
APPNProp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | EdisonLeeeee/Graphgallery | APPNProp | false | 5,099 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
C2 | # 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 co... | devillove084/DeepSignal | C2 | false | 12,258 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
RMulInt | import torch
class RMulInt(torch.nn.Module):
def __init__(self):
super(RMulInt, self).__init__()
def forward(self, x):
return 10 * 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... | ahangchen/torch2trt | RMulInt | false | 6,096 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
IoUnionLoss | # 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... | akanametov/pathgan | IoUnionLoss | false | 18,300 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
AddAndNorm | # 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_... | francismontalbo/attention-is-all-you-need-paper | AddAndNorm | false | 15,365 | [
"MIT"
] | 167 | 21ba3e48917da0c6808126d183bece6a9969cfd2 | https://github.com/francismontalbo/attention-is-all-you-need-paper/tree/21ba3e48917da0c6808126d183bece6a9969cfd2 |
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.autograd import Function
import math
from torch import nn
assert_size... | ArashVahabpour/encoder4editing-contrastive | EqualLinear | false | 13,274 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
minibatch_std_concat_layer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from torch.utils import data as data
import torch.nn as... | achrefjarray/ESRGANplus-master | minibatch_std_concat_layer | false | 1,367 | [
"Apache-2.0"
] | 0 | ba470ec5c565a6dc8b48575b1e185ef6b796aec6 | https://github.com/achrefjarray/ESRGANplus-master/tree/ba470ec5c565a6dc8b48575b1e185ef6b796aec6 |
SConv2d | # 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
assert_size_stride = torch._C._dynamo.guards.a... | sergkuzn148/stg | SConv2d | false | 16,383 | [
"MIT"
] | 96 | 84d9f53ae3665c423836a4d0176dc3b22de62b19 | https://github.com/sergkuzn148/stg/tree/84d9f53ae3665c423836a4d0176dc3b22de62b19 |
GELU | import math
import torch
from torch import nn
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, tensor):
geluPow = tensor + 0.044715 * torch.pow(tensor, 3)
geluTanh = torch.tanh(math.sqrt(2 / math.pi) * geluPow)
geluResult = 1 + geluTan... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | simonepreite/QABERT | GELU | false | 4,334 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
BasicModel6_MultiTensor | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel6_MultiTensor(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input1, input2):
input = input1 + input2
return 1 - F.relu(1 - input)[:, 1]
def get_inputs():
return [tor... | 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... | LMdeLiangMi/captum | BasicModel6_MultiTensor | false | 5,478 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
DQN_hot5 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot5(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-valu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_hot5 | false | 17,188 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
SelfAttn | import torch
from torch import nn
from torch.nn import functional as F
class SelfAttn(nn.Module):
"""
self-attention with learnable parameters
"""
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(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.... | jzhanson/alfred | SelfAttn | false | 3,792 | [
"MIT"
] | 0 | d5b540e7c9b53d3f70cc2907503935fecff00018 | https://github.com/jzhanson/alfred/tree/d5b540e7c9b53d3f70cc2907503935fecff00018 |
GEGLU | # 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 ... | robburdon/pytorch_tabular | GEGLU | false | 16,332 | [
"MIT"
] | 560 | 9bf75f22c6e1b3033ad699713e77c283d55f3555 | https://github.com/robburdon/pytorch_tabular/tree/9bf75f22c6e1b3033ad699713e77c283d55f3555 |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AllenPeng0209/SaccadeNet | SmoothL1Loss | false | 7,651 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
squeeze | import torch
import torch.nn as nn
class squeeze(nn.Module):
def __init__(self, block_size):
super(squeeze, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def inverse(self, input):
output = input.permute(0, 2, 3, 1)
batch_si... | 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... | lingzenan/invertible-resnet | squeeze | false | 7,091 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
mnistmodel_B | import torch
from torch import nn
import torch.nn.functional as F
class mnistmodel_B(nn.Module):
def __init__(self):
super(mnistmodel_B, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=
8, stride=2, padding=3)
self.conv2 = nn.Conv2d(in_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | layel2/layyer-lib | mnistmodel_B | false | 3,877 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
core_network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | felixnon/foveated-visual-attention | core_network | false | 12,366 | [
"MIT"
] | 0 | 7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 | https://github.com/felixnon/foveated-visual-attention/tree/7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 |
Autoencoder | # 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... | JavierAntoran/tiger-costume | Autoencoder | false | 17,458 | [
"MIT"
] | 10 | 975661dfab2c435281f74c6be86529b16881ebcb | https://github.com/JavierAntoran/tiger-costume/tree/975661dfab2c435281f74c6be86529b16881ebcb |
TVLoss | import torch
from torch import nn
import torch.utils.data
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Prajwal564/SRGAN | TVLoss | false | 9,407 | [
"MIT"
] | 0 | 198b86b0cec4d68737f26b190e4ab04887be4ac3 | https://github.com/Prajwal564/SRGAN/tree/198b86b0cec4d68737f26b190e4ab04887be4ac3 |
SEBlock | # 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_... | sysu-shey/ACNet | SEBlock | false | 16,523 | [
"MIT"
] | 767 | 6d967d3fff2d79a37f85799b78a21ffbd9001bd2 | https://github.com/sysu-shey/ACNet/tree/6d967d3fff2d79a37f85799b78a21ffbd9001bd2 |
UpSample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | AlexanderDokuchaev/mmsegmentation | UpSample | false | 11,185 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
ChannelAvgPool | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelAvgPool(nn.AvgPool1d):
def forward(self, input):
n, c, w, h = input.size()
input = input.view(n, c, w * h).permute(0, 2, 1)
pooled = F.avg_pool1d(input, self.kernel_size, self.stride, self.
padding... | 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... | joeization/CycleGAN | ChannelAvgPool | false | 3,752 | [
"MIT"
] | 0 | 9635c8e3a7b1634b2e2eb5b5299f03a4e0786868 | https://github.com/joeization/CycleGAN/tree/9635c8e3a7b1634b2e2eb5b5299f03a4e0786868 |
LeNet_300_100 | # 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.... | VITA-Group/SViTE | LeNet_300_100 | false | 14,553 | [
"MIT"
] | 50 | b0c62fd153c8b0b99917ab935ee76925c9de1149 | https://github.com/VITA-Group/SViTE/tree/b0c62fd153c8b0b99917ab935ee76925c9de1149 |
RewardCriterion | import torch
from torch import nn
from torch.autograd import *
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward, reduction='mean'):
N, L = input.shape[:2]
input = input.gather(2, seq.unsqueeze(2)).squee... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | GeorgeKostenkov/ImageCaptioning.pytorch | RewardCriterion | false | 11,442 | [
"MIT"
] | 0 | 8f17433fdaba2f89774e45ad5a3a88b880932ee6 | https://github.com/GeorgeKostenkov/ImageCaptioning.pytorch/tree/8f17433fdaba2f89774e45ad5a3a88b880932ee6 |
ResidualLayer | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if 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
import math
from torch import Tensor
from torch.nn import Linear
from torch.nn i... | shnhrtkyk/pytorch_geometric | ResidualLayer | false | 10,811 | [
"MIT"
] | 0 | b971fd2ebba10736e6398d6305757be2d81ca681 | https://github.com/shnhrtkyk/pytorch_geometric/tree/b971fd2ebba10736e6398d6305757be2d81ca681 |
RankingLoss | # 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... | KMU-AELAB/Active_Learning | RankingLoss | false | 2,444 | [
"MIT"
] | 0 | bc569c16b5f12b58989a8f3db59b7eb4e35cce1b | https://github.com/KMU-AELAB/Active_Learning/tree/bc569c16b5f12b58989a8f3db59b7eb4e35cce1b |
MultiHeadAttn | import torch
import torch.cuda
from torch import nn
import torch.nn.functional as F
import torch.distributed
import torch.optim
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | admariner/NeMo | MultiHeadAttn | false | 1,394 | [
"Apache-2.0"
] | 0 | e542d7f9063a40afa4119a3b94de4c2c636a37bb | https://github.com/admariner/NeMo/tree/e542d7f9063a40afa4119a3b94de4c2c636a37bb |
L1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CK-er/mmdet | L1Loss | false | 2,063 | [
"Apache-2.0"
] | 0 | 9bea4068efbcf7bf739dbe41917a68d525c29868 | https://github.com/CK-er/mmdet/tree/9bea4068efbcf7bf739dbe41917a68d525c29868 |
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.... | CSLT-THU/Vivi_3.0 | PositionwiseFeedForward | false | 17,029 | [
"Apache-2.0"
] | 3 | 86996d99d662cd33100755501a971c41ce30ca70 | https://github.com/CSLT-THU/Vivi_3.0/tree/86996d99d662cd33100755501a971c41ce30ca70 |
HSwish | # 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.utils.data
import torch
import torch.nn as nn
import torch.nn.functional
imp... | PhelaPoscam/SRGAN-PyTorch | HSwish | false | 957 | [
"Apache-2.0"
] | 0 | c1c68707dbddd1130b2ea71023df748080bcbd52 | https://github.com/PhelaPoscam/SRGAN-PyTorch/tree/c1c68707dbddd1130b2ea71023df748080bcbd52 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(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.... | Chandan-h-509/ignite | Policy | false | 8,976 | [
"BSD-3-Clause"
] | 0 | f8c39828cb1dac49b6ef358cdf77865bf2430106 | https://github.com/Chandan-h-509/ignite/tree/f8c39828cb1dac49b6ef358cdf77865bf2430106 |
MPJPELoss | import torch
import torch.nn as nn
class MPJPELoss(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default:... | 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_... | ZephyrII/mmpose_charger | MPJPELoss | false | 12,029 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
BertPredictionHeadTransform | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Cyndi-Tokyotech/Fin_Text_Analysis_ML | BertPredictionHeadTransform | false | 11,120 | [
"MIT"
] | 0 | 7f9b6c1ea78f8e6f32c003b2de32809722df88d4 | https://github.com/Cyndi-Tokyotech/Fin_Text_Analysis_ML/tree/7f9b6c1ea78f8e6f32c003b2de32809722df88d4 |
Expand | # 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.... | ChaokunChang/SVAS | Expand | false | 248 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
TranLayer | # 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_... | WingsUpete/EEG2Age | TranLayer | false | 5,985 | [
"MIT"
] | 1 | 8d7b9049fe4e47c701659bbbf2843600fa7c8d8d | https://github.com/WingsUpete/EEG2Age/tree/8d7b9049fe4e47c701659bbbf2843600fa7c8d8d |
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