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 |
|---|---|---|---|---|---|---|---|---|---|---|
BinaryNLLEntropy | import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn.modules.loss import _Loss
import torch.jit
class BinaryNLLEntropy(_Loss):
def __init__(self, size_average=True):
super(BinaryNLLEntropy, self).__init__()
self.size_average = size_average
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RoderickGu/Pretraining_GPT | BinaryNLLEntropy | false | 17,851 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
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.nn import Module
from torch.nn import Linear
from torch.nn import Sig... | jfmalloy1/UltraMarathon_Prediction | MLP | false | 3,724 | [
"MIT"
] | 0 | 8eef7bd2860ce255994d32a0150c09b3b655cee7 | https://github.com/jfmalloy1/UltraMarathon_Prediction/tree/8eef7bd2860ce255994d32a0150c09b3b655cee7 |
LinearVariance | # 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 ... | AlliedToasters/elko_den | LinearVariance | false | 7,678 | [
"Apache-2.0"
] | 38 | 4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 | https://github.com/AlliedToasters/elko_den/tree/4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 |
PointWiseFeedForward | import torch
class PointWiseFeedForward(torch.nn.Module):
def __init__(self, hidden_units, dropout_rate):
super(PointWiseFeedForward, self).__init__()
self.conv1 = torch.nn.Conv1d(hidden_units, hidden_units, kernel_size=1)
self.dropout1 = torch.nn.Dropout(p=dropout_rate)
self.relu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Vivdaddy/recsys-filterbubbles | PointWiseFeedForward | false | 5,940 | [
"MIT"
] | 1 | d21639bce515ffef5ba2db530dc2505eee1f83c0 | https://github.com/Vivdaddy/recsys-filterbubbles/tree/d21639bce515ffef5ba2db530dc2505eee1f83c0 |
Residual | import torch
import torch.nn as nn
class Residual(nn.Sequential):
""" Residual block that runs like a Sequential, but then adds the original input to the output tensor.
See :class:`torch.nn.Sequential` for more information.
Warning:
The dimension between the input and output of the mo... | 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... | FenryrMKIII/objectDetection-lightnet | Residual | false | 2,242 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
conv_head_pooling | import torch
import torch.nn as nn
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, conv_type,
padding_mode='zeros', dilation=1):
super(conv_head_pooling, self).__init__()
if conv_type == 'depthwise':
_groups = in_feature
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | yasarniyazoglu/d2go | conv_head_pooling | false | 11,032 | [
"Apache-2.0"
] | 0 | 308c2700c51c70a7a928d99a477b64e856d1ed5e | https://github.com/yasarniyazoglu/d2go/tree/308c2700c51c70a7a928d99a477b64e856d1ed5e |
my_Layernorm | import torch
import torch.nn as nn
class my_Layernorm(nn.Module):
"""
Special designed layernorm for the seasonal part
"""
def __init__(self, channels):
super(my_Layernorm, self).__init__()
self.layernorm = nn.LayerNorm(channels)
def forward(self, x):
x_hat = self.layerno... | 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_... | thuml/Autoformer | my_Layernorm | false | 16,590 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
SEModule | # 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_... | ChrisLiu007/Pytorch-Code-Template | SEModule | false | 5,008 | [
"MIT"
] | 1 | 25eae3ffe43f60a4f7e06651e3a3cd5d0b69b9ae | https://github.com/ChrisLiu007/Pytorch-Code-Template/tree/25eae3ffe43f60a4f7e06651e3a3cd5d0b69b9ae |
TestNet | import torch
from torch import nn
class TestNet(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv1d(1, 1, 1)
def forward(self, x):
x_len = x.shape[-1]
return self.conv(x.view(-1, 1, x_len)).view(x.shape)
def get_inputs():
return [torch.rand([4, 4, 4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | TuZehai/pytorch_stoi | TestNet | false | 14,515 | [
"MIT"
] | 45 | ae58e3ef4d608fc367e522150f48c58f122716fd | https://github.com/TuZehai/pytorch_stoi/tree/ae58e3ef4d608fc367e522150f48c58f122716fd |
Discriminator | # 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... | tasfia/BMCoGAN | Discriminator | false | 13,107 | [
"MIT"
] | 0 | 0d400c2c71dbfb69af422afc487f65afb98de8af | https://github.com/tasfia/BMCoGAN/tree/0d400c2c71dbfb69af422afc487f65afb98de8af |
PointwiseConvolutionLayer | # 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
as... | slgao/FU-DeepLearningCourse | PointwiseConvolutionLayer | false | 4,360 | [
"MIT"
] | 0 | 2300e8bdaa2afb4c73535d5de80874f6103af6f2 | https://github.com/slgao/FU-DeepLearningCourse/tree/2300e8bdaa2afb4c73535d5de80874f6103af6f2 |
GCNModelAE | # 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.... | chundiliu/random_rewrite | GCNModelAE | false | 1,715 | [
"MIT"
] | 0 | fd106642da82b0ad42b8b0fa405147b321d67cbb | https://github.com/chundiliu/random_rewrite/tree/fd106642da82b0ad42b8b0fa405147b321d67cbb |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kama1kant/rl-autonomous-driving | Actor | false | 10,356 | [
"MIT"
] | 0 | 8f8687ff81892874a32c6a556c6be2e686012731 | https://github.com/kama1kant/rl-autonomous-driving/tree/8f8687ff81892874a32c6a556c6be2e686012731 |
_Residual_Block | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=256, out_channels=256,
kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | twtygqyy/pytorch-EDSR | _Residual_Block | false | 16,661 | [
"MIT"
] | 59 | 001031b6563fcc45d4e7edb7e14c41fb9982ce64 | https://github.com/twtygqyy/pytorch-EDSR/tree/001031b6563fcc45d4e7edb7e14c41fb9982ce64 |
BahdanauAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
class BahdanauAttention(nn.Module):
def __init__(self, hidden_size):
super(BahdanauAttention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
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.... | Chiang97912/seq2seq | BahdanauAttention | false | 268 | [
"MIT"
] | 0 | 4b544016ecc16fa8e48358021cf486e58494aa0f | https://github.com/Chiang97912/seq2seq/tree/4b544016ecc16fa8e48358021cf486e58494aa0f |
AddNet | import torch
import torch.nn.functional
class AddNet(torch.nn.Module):
def __init__(self):
super(AddNet, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 4, kernel_size=1, stride=1)
self.conv2 = torch.nn.Conv2d(3, 4, kernel_size=1, stride=1)
def forward(self, x, y):
x = self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_s... | elad-c/model_optimization | AddNet | false | 10,646 | [
"Apache-2.0"
] | 0 | b0ecf41c3f9434008d57d7fe724ff8585e19d4cc | https://github.com/elad-c/model_optimization/tree/b0ecf41c3f9434008d57d7fe724ff8585e19d4cc |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Zenodia/NeMo | MultiHeadAttention | false | 1,329 | [
"Apache-2.0"
] | 0 | 3c288d8a7caf667c95444c39434e3ebc5f53d911 | https://github.com/Zenodia/NeMo/tree/3c288d8a7caf667c95444c39434e3ebc5f53d911 |
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Bhaskers-Blu-Org2/metric-transfer.pytorch | Normalize | false | 13,394 | [
"MIT"
] | 51 | b0ae8ed6e6f62357100d799defbb61a78c831a87 | https://github.com/Bhaskers-Blu-Org2/metric-transfer.pytorch/tree/b0ae8ed6e6f62357100d799defbb61a78c831a87 |
TreeLSTM | # 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 ... | mhoangvslev/torchfold | TreeLSTM | false | 16,040 | [
"Apache-2.0"
] | 160 | 9285c7889f2e1966fb94c4b8a3e91bcd60e40ab2 | https://github.com/mhoangvslev/torchfold/tree/9285c7889f2e1966fb94c4b8a3e91bcd60e40ab2 |
AlphaClassifier | import torch
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset
def compute_auc(labels, scores, pos_label=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
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
fro... | vitskvara/shape-guided-anomaly-detection | AlphaClassifier | false | 4,751 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
OptimizedMLP | # 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.optim
... | plaveczlambert/nonlinearbubbledynamics | OptimizedMLP | false | 10,695 | [
"MIT"
] | 0 | 190c5170f7ff6068badeee818c01226c55aaec97 | https://github.com/plaveczlambert/nonlinearbubbledynamics/tree/190c5170f7ff6068badeee818c01226c55aaec97 |
AlphaChooser | import torch
from torch import nn
class AlphaChooser(torch.nn.Module):
"""
It manages the alpha values in alpha-entmax
function.
"""
def __init__(self, head_count):
super(AlphaChooser, self).__init__()
self.pre_alpha = nn.Parameter(torch.randn(head_count))
def forward(self):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | prajjwal1/fluence2 | AlphaChooser | false | 16,276 | [
"Apache-2.0"
] | 64 | f7353f4947ac4712ecd1df34e97df27d83060f13 | https://github.com/prajjwal1/fluence2/tree/f7353f4947ac4712ecd1df34e97df27d83060f13 |
ResidualDenseBlock_5C | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | LenKerr/Semantic-Colorization-GAN | ResidualDenseBlock_5C | false | 5,528 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
InstanceNormalization | # 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_... | E-Dreamer-LQ/Astronomical_Target_Detection | InstanceNormalization | false | 17,234 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 |
Bias | import torch
import torch.nn as nn
class Bias(nn.Module):
def __init__(self):
super(Bias, self).__init__()
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
B, C, H, W = feat_sound.size()
feat_img = feat_img.view(B, 1, C)
z = torch.bmm(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | TaoStarlit/Sound-of-Pixels | Bias | false | 11,921 | [
"MIT"
] | 0 | 06cd37a75836e22208f2e59bcc263b89938e065e | https://github.com/TaoStarlit/Sound-of-Pixels/tree/06cd37a75836e22208f2e59bcc263b89938e065e |
DenseSAGEConv | import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch import... | CFF-Dream/pytorch_geometric | DenseSAGEConv | false | 2,033 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
Temporal_Attention_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | msalvato/pytorch_geometric_temporal | Temporal_Attention_layer | false | 10,591 | [
"MIT"
] | 0 | 149bd46d3b2bddfc3570e31a91a3f53e8873d50e | https://github.com/msalvato/pytorch_geometric_temporal/tree/149bd46d3b2bddfc3570e31a91a3f53e8873d50e |
ResolutionScalingLayer | # 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | NejcHirci/material-addon | ResolutionScalingLayer | false | 17,768 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
ODEfunc | # 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.... | BoyanJIANG/4D-Compositional-Representation | ODEfunc | false | 7,836 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
RegModel | # 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_... | amperie/user-models | RegModel | false | 3,092 | [
"Apache-2.0"
] | 0 | 5236c50d0f20a7bac81acc5d1936a3502de2f5f3 | https://github.com/amperie/user-models/tree/5236c50d0f20a7bac81acc5d1936a3502de2f5f3 |
TripletSemihardLoss | import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.model_zoo
def pdist(A, squared=False, eps=0.0001):
prod = torch.mm(A, A.t())
norm = prod.diag().unsqueeze(1).expand_as(prod)
res = (norm + norm.t() - 2 * prod).clamp(min=0)
if squared:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CompVis/metric-learning-divide-and-conquer-improved | TripletSemihardLoss | false | 8,193 | [
"MIT"
] | 11 | 33fe768a54376a090e2d7139898177b06e8903d2 | https://github.com/CompVis/metric-learning-divide-and-conquer-improved/tree/33fe768a54376a090e2d7139898177b06e8903d2 |
MultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | pengchengguo/wenet | MultiHeadedAttention | false | 16,236 | [
"Apache-2.0"
] | 1,166 | 940dc164e5cfa9b8c0131688f0f9457af9563892 | https://github.com/pengchengguo/wenet/tree/940dc164e5cfa9b8c0131688f0f9457af9563892 |
MemoryDictionary | # 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.nn.functional as F
from typing import *
asser... | Geson-anko/ThinkingSimulation | MemoryDictionary | false | 11,454 | [
"MIT"
] | 0 | bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 | https://github.com/Geson-anko/ThinkingSimulation/tree/bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Saran-nns/delve | Net | false | 1,032 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
Selection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | HendrikPN/reinforced_scinet | Selection | false | 17,375 | [
"Apache-2.0"
] | 4 | b57c9d1d997cc56647db4faa0690364e7039a5ee | https://github.com/HendrikPN/reinforced_scinet/tree/b57c9d1d997cc56647db4faa0690364e7039a5ee |
DPLSTMCell | # 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 ... | romovpa/opacus | DPLSTMCell | false | 10,860 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 |
RegLoss | import torch
import torch.nn as nn
import torch.utils.data
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
... | 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
... | Ssong24/CenterNet_Custom | RegLoss | false | 9,544 | [
"MIT"
] | 0 | 526ec70f8dfabf9fb9179c9be28ce50fb2a7961c | https://github.com/Ssong24/CenterNet_Custom/tree/526ec70f8dfabf9fb9179c9be28ce50fb2a7961c |
CnptAttention | import torch
from torch import nn
class CnptAttention(nn.Module):
def __init__(self, in_dim, out_dim):
super(CnptAttention, self).__init__()
self.softmax = nn.Softmax(dim=-1)
def forward(self, query, key):
"""
query: sent_emb (1, D)
key: [(k, D), (k,D)]
value:... | 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... | adonis704/ucas_2021_hc_15 | CnptAttention | false | 18,225 | [
"MIT"
] | 6 | 7308c3b32962ef5430d85ccfcb199ebe40bf4a7f | https://github.com/adonis704/ucas_2021_hc_15/tree/7308c3b32962ef5430d85ccfcb199ebe40bf4a7f |
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 numpy as np
import tor... | rbak/deep-rl-udacity-project-3 | Critic | false | 12,927 | [
"MIT"
] | 0 | 4bf2aec6b0ef27636ebd11dfd4b442554208cffb | https://github.com/rbak/deep-rl-udacity-project-3/tree/4bf2aec6b0ef27636ebd11dfd4b442554208cffb |
HFM | import torch
import torch.nn as nn
import torch.utils.model_zoo
class HFM(nn.Module):
def __init__(self, k=2):
super().__init__()
self.k = k
self.net = nn.Sequential(nn.AvgPool2d(kernel_size=self.k, stride=
self.k), nn.Upsample(scale_factor=self.k, mode='nearest'))
def fo... | 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.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NawaNae/ESRT-Huawei | HFM | false | 2,680 | [
"MIT"
] | 0 | edea1c0bafec940dc7ea8e5110c355a83188665c | https://github.com/NawaNae/ESRT-Huawei/tree/edea1c0bafec940dc7ea8e5110c355a83188665c |
SpatialLogMeanExp | # 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 numpy as np
imp... | CPJKU/kagglebirds2020 | SpatialLogMeanExp | false | 17,030 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
PyConv3 | # 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.nn.parallel
import torch.optim
import torch.u... | lkf59553/pyconv | PyConv3 | false | 15,944 | [
"MIT"
] | 295 | d8b39cf43014b8fd277dcefc9eb7f8880511e977 | https://github.com/lkf59553/pyconv/tree/d8b39cf43014b8fd277dcefc9eb7f8880511e977 |
DenseModel | # 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... | HSE-LAMBDA/pytorch_ard | DenseModel | false | 9,044 | [
"MIT"
] | 0 | b6b40d4c495d3374180698549d8fef0b768ffd3a | https://github.com/HSE-LAMBDA/pytorch_ard/tree/b6b40d4c495d3374180698549d8fef0b768ffd3a |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
self.beta = nn.Parameter(torch.tensor(1.0))
def forward(self, x):
return x * torch.sigmoid(self.beta * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ahmedfgad/high-fidelity-generative-compression | Swish | false | 6,117 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
ConvGRUCellNd | # 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 ... | ankmathur96/torchsupport | ConvGRUCellNd | false | 3,189 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
KnowledgeDistillationLoss | import torch
import torch.nn as nn
class KnowledgeDistillationLoss(nn.Module):
def __init__(self, reduction='mean', alpha=1.0):
super().__init__()
self.reduction = reduction
self.alpha = alpha
def forward(self, inputs, targets, mask=None):
inputs = inputs.narrow(1, 0, targets... | 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
... | VitoPalmisano/MiB_BiSeNet_SEAM_test | KnowledgeDistillationLoss | false | 2,946 | [
"MIT"
] | 0 | 7b74beb69f135c0bb843ee24c90c3097ce448eec | https://github.com/VitoPalmisano/MiB_BiSeNet_SEAM_test/tree/7b74beb69f135c0bb843ee24c90c3097ce448eec |
MultiHeadedAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, num_head, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % num_head == 0
self.d_k = d_model // num_head
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.... | bekirufuk/pointer_summarizer | MultiHeadedAttention | false | 12,174 | [
"Apache-2.0"
] | 0 | 8fc9726f9337b26339848d896a09e7e8f9456bcc | https://github.com/bekirufuk/pointer_summarizer/tree/8fc9726f9337b26339848d896a09e7e8f9456bcc |
AttentionPool2d | # 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.... | AranKomat/Diff-DALLE | AttentionPool2d | false | 13,349 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
Mean | # 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
reinterpret... | Tahlor/glom-pytorch | Mean | false | 1,125 | [
"MIT"
] | 0 | 45b2fc52af5288cd53611e497a70d53ffa303410 | https://github.com/Tahlor/glom-pytorch/tree/45b2fc52af5288cd53611e497a70d53ffa303410 |
HardAttn | import torch
from torch.nn import functional as F
import torch.nn as nn
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
def init_params(self)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | RndmVariableQ/deep-person-reid | HardAttn | false | 11,869 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
def __init__(self, state_size, action_size, seed, fcs1_units=512,
fc2_unit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | asiliskender/deep-reinforcement-learning | Critic | false | 12,132 | [
"MIT"
] | 0 | dbf96d67477aa9242128b78b081474193e1e4538 | https://github.com/asiliskender/deep-reinforcement-learning/tree/dbf96d67477aa9242128b78b081474193e1e4538 |
BinaryLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def adjust_smooth_l1_loss(y_pred, theta=0.1):
less_grad_factor = 1.0 / (2 * theta)
less_loss_bias = less_grad_factor * theta ** 2
less_than_theta = (y_pred < theta).float()
loss = less_than_theta * y_pred ** 2 * less_grad_factor + (1 -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride ... | DerekRay/2020-instanceSeg | BinaryLoss | false | 7,952 | [
"MIT"
] | 25 | a08ad95e64726db53cc32a5f90aaa13ae3cdb6a3 | https://github.com/DerekRay/2020-instanceSeg/tree/a08ad95e64726db53cc32a5f90aaa13ae3cdb6a3 |
LinearEnsemble | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as T
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | RamiSketcher/AMMI-RL | LinearEnsemble | false | 975 | [
"MIT"
] | 0 | 6d51587ff4d5dc14cba87fca561bd7b340b44586 | https://github.com/RamiSketcher/AMMI-RL/tree/6d51587ff4d5dc14cba87fca561bd7b340b44586 |
SpectralEigenConv | import torch
import torch.nn as nn
class SpectralEigenConv(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/GraphGallery | SpectralEigenConv | false | 13,650 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
LipNormLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | rh-ia/color-information | LipNormLinear | false | 4,291 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 |
ClassHead | import torch
import torch.nn as nn
from itertools import product as product
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | Akshobhya2018eeb1137/Attendance_System_Using_Face_Recognition | ClassHead | false | 18,444 | [
"MIT"
] | 2 | a52ca53e15332ab706f6ed23045b38ea6d38dfd9 | https://github.com/Akshobhya2018eeb1137/Attendance_System_Using_Face_Recognition/tree/a52ca53e15332ab706f6ed23045b38ea6d38dfd9 |
Generator | import torch
from torch import nn
import torch.nn.functional as f
class Generator(nn.Module):
def __init__(self, nz):
super(Generator, self).__init__()
self.fc1 = nn.Linear(nz, 10)
self.fc2 = nn.Linear(10, 1)
def forward(self, x):
x = f.relu(self.fc1(x))
x = self.fc2(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Anas-Alamri/vegans | Generator | false | 4,837 | [
"MIT"
] | 1 | 2e8513c9cbebf18d0125cebdc7d924dd6345883a | https://github.com/Anas-Alamri/vegans/tree/2e8513c9cbebf18d0125cebdc7d924dd6345883a |
TimeEncoding | import torch
import torch.nn as nn
class TimeEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(TimeEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, mask, lengths):
time = mask * 1 / (lengths[..., None] - 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Immocat/ACTOR | TimeEncoding | false | 13,829 | [
"MIT"
] | 164 | c7237e82e333bf2c57f7d8e12f27d0831233befc | https://github.com/Immocat/ACTOR/tree/c7237e82e333bf2c57f7d8e12f27d0831233befc |
BetaIntersection | # 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.... | amayuelas/NNKGReasoning | BetaIntersection | false | 6,189 | [
"MIT"
] | 1 | 0e3623b344fd4e3088ece897f898ddbb1f80888d | https://github.com/amayuelas/NNKGReasoning/tree/0e3623b344fd4e3088ece897f898ddbb1f80888d |
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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | DDGRCF/YOLOX_OBB | Expand | false | 7,943 | [
"Apache-2.0"
] | 39 | 27b80953306492b8bc83b86b1353d8cee01ef9b6 | https://github.com/DDGRCF/YOLOX_OBB/tree/27b80953306492b8bc83b86b1353d8cee01ef9b6 |
ComplexConv | # 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... | wizofe/urus-mri-recon | ComplexConv | false | 4,539 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
SourceContextGate | # 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 ... | NaomiatLibrary/OpenNMT-kpg-release | SourceContextGate | false | 875 | [
"MIT"
] | 0 | 1da3468d7dad22529a77f3526abf9b373bd3dc4c | https://github.com/NaomiatLibrary/OpenNMT-kpg-release/tree/1da3468d7dad22529a77f3526abf9b373bd3dc4c |
TripletLoss | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
def euclidean_dist(x: 'Tensor', y: 'Tensor') ->Tensor:
xx, yy = torch.meshgrid((x ** 2).sum(1), (y ** 2).sum(1))
return xx + yy - 2 * (x @ y.t())
class TripletLoss(nn.Module):
"""
Modified from Tong Xiao'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Tensor
from... | sithu31296/re_identification | TripletLoss | false | 4,342 | [
"MIT"
] | 0 | 28c2cf32c6c8c9d79330e1419a7156fe10d8ac95 | https://github.com/sithu31296/re_identification/tree/28c2cf32c6c8c9d79330e1419a7156fe10d8ac95 |
Conv2dWithFastWeight | # 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 typing import Tuple
from typing import Union
import torch.nn as nn
assert_s... | BIGWangYuDong/mmfewshot | Conv2dWithFastWeight | false | 13,361 | [
"Apache-2.0"
] | 376 | dac097afc92df176bc2de76b7c90968584865197 | https://github.com/BIGWangYuDong/mmfewshot/tree/dac097afc92df176bc2de76b7c90968584865197 |
Transformer | # 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.... | bruchano/ImageStyler | Transformer | false | 9,945 | [
"MIT"
] | 0 | 7bde13bc954566088c477065adb5c4e4214c28bb | https://github.com/bruchano/ImageStyler/tree/7bde13bc954566088c477065adb5c4e4214c28bb |
SALayer | import torch
import torch.nn as nn
import torch.utils.model_zoo
class SALayer(nn.Module):
def __init__(self, kernel_size=7):
super(SALayer, self).__init__()
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | JiahangGu/RFN | SALayer | false | 17,477 | [
"MIT"
] | 4 | 8f7b33e22bb0a9f4057476720e05cc694a46ec00 | https://github.com/JiahangGu/RFN/tree/8f7b33e22bb0a9f4057476720e05cc694a46ec00 |
LipNormConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
def _max_except_dim(input, dim):
maxed = input
for axis in range(input.ndimension() - 1, dim, -1):
maxed, _ = maxed.max(axis, keepdim=True)
for axis in range(dim - 1, -1, -1):
maxed, _ = maxed.max(axis, keepdim=True)
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | zxydi1992/residual-flows | LipNormConv2d | false | 13,198 | [
"MIT"
] | 0 | 4ec289681dc91cff5312b22f7ebed93838b440fb | https://github.com/zxydi1992/residual-flows/tree/4ec289681dc91cff5312b22f7ebed93838b440fb |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | uber-common/safemutations | LayerNorm | false | 16,639 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
RMSE | import torch
import torch.nn.functional as F
import torch.nn as nn
class RMSE(nn.Module):
def __init__(self):
super(RMSE, self).__init__()
def forward(self, fake, real):
if not fake.shape == real.shape:
_, _, H, W = real.shape
fake = F.upsample(fake, size=(H, W), mode... | 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 | false | 723 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ardihikaru/learn-to-cluster | BasicBlock | false | 6,230 | [
"MIT"
] | 1 | d7a5ea0946f7b402f8878bfd608bf3e0dc9a26ca | https://github.com/ardihikaru/learn-to-cluster/tree/d7a5ea0946f7b402f8878bfd608bf3e0dc9a26ca |
FeatExemplarAvgBlock | import torch
import torch.nn as nn
class FeatExemplarAvgBlock(nn.Module):
def __init__(self, nFeat):
super(FeatExemplarAvgBlock, self).__init__()
def forward(self, features_train, labels_train):
labels_train_transposed = labels_train.transpose(1, 2)
weight_novel = torch.bmm(labels_tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | CSer-Tang-hao/FS-KTN | FeatExemplarAvgBlock | false | 7,869 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
TemporalDecay | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class TemporalDecay(nn.Module):
def __init__(self, input_size, rnn_hid_size):
super(TemporalDecay, self).__init__()
self.rnn_hid_size = rnn_hid_size
self.build(input_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sobhan1996/BRITS-master | TemporalDecay | false | 9,511 | [
"MIT"
] | 0 | 66726ec104dad43c6d8367b0c9ef8f19daf65f0e | https://github.com/Sobhan1996/BRITS-master/tree/66726ec104dad43c6d8367b0c9ef8f19daf65f0e |
LandmarkHead | import torch
from torch import nn
import torch.cuda
class LandmarkHead(nn.Module):
"""
LandmarkHead
RetinaFace head for landmark branch.
inchannels (`int`):
number of input channels.
num_anchors (`int`):
number of anchors.
"""
def __init__(self, inchannel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.cuda
assert_size_stride = torch._C._dynamo.gua... | LoveEachDay/towhee | LandmarkHead | false | 11,717 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
policy_net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bigtreeljc/force_learning | policy_net | false | 3,209 | [
"MIT"
] | 0 | 183a7c96c411e282966604e3cb375ba49e91a88c | https://github.com/bigtreeljc/force_learning/tree/183a7c96c411e282966604e3cb375ba49e91a88c |
PixelwiseLossMSE | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | akanametov/pathgan | PixelwiseLossMSE | false | 18,295 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
Atan | import torch
import torch.nn as nn
class Atan(nn.Module):
def forward(self, x):
return torch.atan(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | awlange/pysurvival | Atan | false | 14,916 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
SimpleReshapeModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReshapeModel(torch.nn.Module):
def __init__(self, shape):
super(SimpleReshapeModel, self).__init__()
self.shape = shape
def forward(self, tensor):
combined = tensor + tensor
return combined.reshape(se... | 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... | YaronBenAtar/glow | SimpleReshapeModel | false | 14,684 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
AconC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | IanVzs/labelImg | AconC | false | 11,505 | [
"MIT"
] | 0 | 3d3dfbf9cf385f38c60376826fdce1f178f563a6 | https://github.com/IanVzs/labelImg/tree/3d3dfbf9cf385f38c60376826fdce1f178f563a6 |
BinaryMul | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMul | false | 11,564 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | QiuhongAnnaWei/IBRNet | MultiHeadAttention | false | 14,276 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
NN | import torch
from torch import nn
import torch.nn.functional as F
class NN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 16)
self.fc2 = nn.Linear(16, 3)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Jie-Yuan/Deeps | NN | false | 17,478 | [
"MIT"
] | 4 | b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | https://github.com/Jie-Yuan/Deeps/tree/b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
"""Initialize parameters and build model.
Args:
state_dim (int): Dimension of each state
action_dim (int): Dimension of each action
Return:
value output of 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | sofya-pugach/spot_mini_mini | Critic | false | 16,485 | [
"MIT"
] | 323 | 42770145e91ed2625ccc7e4f4d7016ce14a61464 | https://github.com/sofya-pugach/spot_mini_mini/tree/42770145e91ed2625ccc7e4f4d7016ce14a61464 |
GradientLoss | import torch
from torch import nn
class GradientLoss(nn.Module):
"""
L1 loss on the gradient of the picture
"""
def __init__(self):
super(GradientLoss, self).__init__()
def forward(self, a):
gradient_a_x = torch.abs(a[:, :, :, :-1] - a[:, :, :, 1:])
gradient_a_y = torch.a... | 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_... | GuYuanjie/Deep-Retinex-fusion | GradientLoss | false | 17,340 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
MaxNormConstraintLinear | import torch
import torch.nn as nn
class MaxNormConstraintLinear(nn.Linear):
def __init__(self, *args, max_norm_value=1, norm_axis=0, **kwargs):
self.max_norm_value = max_norm_value
self.norm_axis = norm_axis
super().__init__(*args, **kwargs)
def forward(self, input):
self.we... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Mrswolf/brainda | MaxNormConstraintLinear | false | 8,567 | [
"MIT"
] | 24 | cbd2fa6334d9e6243324dbaf086be4eb4047e801 | https://github.com/Mrswolf/brainda/tree/cbd2fa6334d9e6243324dbaf086be4eb4047e801 |
four_layer_conv | import torch
class four_layer_conv(torch.nn.Module):
def __init__(self):
super(four_layer_conv, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.fcn1 = torch.nn.Conv2d(256, 256, 3, stride=1, padding=1)
self.fcn2 = torch.nn.Conv2d(256, 256, 3, stride=1, padding=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 import triton_helpers
assert_size_stride = torch._C... | peckjon/detectorch | four_layer_conv | false | 16,270 | [
"Apache-2.0"
] | 627 | 69d31250d79a72b12b7419638ef59163f833bbba | https://github.com/peckjon/detectorch/tree/69d31250d79a72b12b7419638ef59163f833bbba |
ClassHead | import torch
import torch.nn as nn
from itertools import product as product
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | huigs/retinaface-pytorch | ClassHead | false | 10,246 | [
"MIT"
] | 0 | 0d7551d5863d172c2122bdd8d2d58be36e1b10fd | https://github.com/huigs/retinaface-pytorch/tree/0d7551d5863d172c2122bdd8d2d58be36e1b10fd |
Policy | import torch
from copy import deepcopy
import torch.nn as nn
from typing import *
import torch.utils
import torch.optim
class Policy(nn.Module):
def __init__(self, max_nodes, search_space):
super(Policy, self).__init__()
self.max_nodes = max_nodes
self.search_space = deepcopy(search_space... | 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 copy import deepc... | Mirofil/AutoDL-Projects | Policy | false | 2,654 | [
"MIT"
] | 0 | e7ee9fe27e5c5561a4b9fd1c1ee185677ef30893 | https://github.com/Mirofil/AutoDL-Projects/tree/e7ee9fe27e5c5561a4b9fd1c1ee185677ef30893 |
KLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KLLoss(nn.Module):
"""
KL-Divergence symmetric loss between two distributions
Used in here for knowledge distillation
"""
def __init__(self):
super(KLLoss, self).__init__()
self.similarity_f = nn.CosineSimila... | 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... | NeurAI-Lab/DoGo | KLLoss | false | 17,764 | [
"MIT"
] | 3 | e3038204f15a40a2d5caca20bb171c87a40d95ba | https://github.com/NeurAI-Lab/DoGo/tree/e3038204f15a40a2d5caca20bb171c87a40d95ba |
InitConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class InitConv(nn.Module):
def __init__(self, in_channels=4, out_channels=16, dropout=0.2):
super(InitConv, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
padding=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
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | felixquinton1/TransBTS | InitConv | false | 10,173 | [
"Apache-2.0"
] | 0 | 6992c902413ba15f40ebfe9f6d5d0e3594051033 | https://github.com/felixquinton1/TransBTS/tree/6992c902413ba15f40ebfe9f6d5d0e3594051033 |
SimpleConvTranspose2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConvTranspose2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, output_padding=0, dilation=1,
groups=1):
super(SimpleConvTranspose2dModule, self).__init__()
self.str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | andreas-hommel/glow | SimpleConvTranspose2dModule | false | 3,325 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
AnchorBoxTransform | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
class AnchorBoxTransform(nn.Module):
def __init__(self, mean: 'Optional[Tensor]'=None, std:
'Optional[Tensor]'=None, log_length: 'bool'=False):
super(AnchorBoxTransform, self).__init__()
self.mean = me... | 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 Tensor
from typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | TidalPaladin/combustion | AnchorBoxTransform | false | 17,985 | [
"Apache-2.0"
] | 3 | 69b9a2b9baf90b81ed9098b4f0391f5c15efaee7 | https://github.com/TidalPaladin/combustion/tree/69b9a2b9baf90b81ed9098b4f0391f5c15efaee7 |
NatureHead | import torch
import torch.nn as nn
import torch.nn.functional as F
class NatureHead(torch.nn.Module):
""" DQN Nature 2015 paper
input: [None, 84, 84, 4]; output: [None, 3136] -> [None, 512];
"""
def __init__(self, n):
super(NatureHead, self).__init__()
self.conv1 = nn.Conv2d(n, 32... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | andy920262/pytorch-a2c-ppo-acktr | NatureHead | false | 12,095 | [
"MIT"
] | 0 | 2e7e85219dfe737cb4036de3cf0c8b00706d640e | https://github.com/andy920262/pytorch-a2c-ppo-acktr/tree/2e7e85219dfe737cb4036de3cf0c8b00706d640e |
Quantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | yzxing87/Invertible-ISP | Quantization | false | 16,793 | [
"MIT"
] | 246 | 344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | https://github.com/yzxing87/Invertible-ISP/tree/344dd333dd2a075f6a9e4ffc445dc387ca3014c4 |
MaxPool | # 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.utils.data
import torch.utils
import torch.cuda
assert_... | chomin/BayesNAS | MaxPool | false | 3,293 | [
"Apache-2.0"
] | 0 | 7b1d991d1e10213fa999eab513d1e12fe4bb571b | https://github.com/chomin/BayesNAS/tree/7b1d991d1e10213fa999eab513d1e12fe4bb571b |
LayerThreshold | # 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | LayerThreshold | false | 15,149 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
StatsPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.... | suissemaxx/pyannote-audio-develop_colab | StatsPool | false | 4,386 | [
"MIT"
] | 0 | e9499372a1771c21e1604424a6dd041337111093 | https://github.com/suissemaxx/pyannote-audio-develop_colab/tree/e9499372a1771c21e1604424a6dd041337111093 |
HingeGANLossDiscriminator | import torch
import torch.nn as nn
class HingeGANLossDiscriminator(nn.Module):
"""
This class implements the Hinge discriminator GAN loss proposed in:
https://arxiv.org/pdf/1705.02894.pdf
"""
def __init__(self) ->None:
"""
Constructor method.
"""
super(HingeGANLoss... | 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... | ChristophReich1996/Mode_Collapse | HingeGANLossDiscriminator | false | 7,901 | [
"MIT"
] | 14 | 937ee8bf96510fbf4070fc7e14b78276ab036b8c | https://github.com/ChristophReich1996/Mode_Collapse/tree/937ee8bf96510fbf4070fc7e14b78276ab036b8c |
TemporalAttention | import torch
from torch import nn
class TemporalAttention(nn.Module):
"""
Temporal attention module from https://dl.acm.org/doi/abs/10.1145/3448083
"""
def __init__(self, hidden_dim):
super(TemporalAttention, self).__init__()
self.fc = nn.Linear(hidden_dim, 1)
self.sm = torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | STRCSussex-UbiCompSiegen/dl_har_model | TemporalAttention | false | 2,866 | [
"MIT"
] | 0 | caac0f87fc7dd08a5d6ad3e4455ee25b35f5e7b4 | https://github.com/STRCSussex-UbiCompSiegen/dl_har_model/tree/caac0f87fc7dd08a5d6ad3e4455ee25b35f5e7b4 |
SelfAttentionFuseLayer | import torch
from torch import nn
class SelfAttentionFuseLayer(nn.Module):
def __init__(self, dim):
super(SelfAttentionFuseLayer, self).__init__()
self.W_7 = nn.Linear(dim, dim)
self.w_8 = nn.Linear(dim, 1)
self.activation = nn.Tanh()
def forward(self, hidden_states):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nju-websoft/Jeeves | SelfAttentionFuseLayer | false | 12,834 | [
"Apache-2.0"
] | 0 | 6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 | https://github.com/nju-websoft/Jeeves/tree/6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class IoULoss(nn.Module):
"""
Creates a criterion that computes the Intersection over Union (IoU)
between a segmentation mask and its ground truth.
Rahman... | 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.nn.parallel
import torch.optim
import torch.utils.data... | TransformersWsz/onestage_grounding | IoULoss | false | 2,916 | [
"MIT"
] | 0 | c939a7d5d7c7f9e1bfa8df2e6269397b8f840b5a | https://github.com/TransformersWsz/onestage_grounding/tree/c939a7d5d7c7f9e1bfa8df2e6269397b8f840b5a |
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