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 |
|---|---|---|---|---|---|---|---|---|---|---|
Envelope | import torch
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | MINATILO/pytroch-geometric | Envelope | false | 9,383 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
Softplus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch.utils.data import Dataset as Dat... | KelvinKan/CP-Flow | Softplus | false | 13,931 | [
"MIT"
] | 64 | d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 | https://github.com/KelvinKan/CP-Flow/tree/d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 |
UnpackLayerConv2d | import torch
import torch.nn as nn
class Conv2D(nn.Module):
"""
2D convolution with GroupNorm and ELU
Parameters
----------
in_channels : int
Number of input channels
out_channels : int
Number of output channels
kernel_size : int
Kernel size
stride : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | pection/packnet-sfm | UnpackLayerConv2d | false | 7,459 | [
"MIT"
] | 1 | d5673567b649e6bfda292c894cacdeb06aa80913 | https://github.com/pection/packnet-sfm/tree/d5673567b649e6bfda292c894cacdeb06aa80913 |
Symmetric | import torch
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Symmetric(nn.Module):
def forward(self, X):
return X.triu() + X.triu(1).transpose(-1, -2)
def ge... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Ismail-Mustapha/tutorials | Symmetric | false | 13,849 | [
"BSD-3-Clause"
] | 6,424 | 0ccfbf0047db855e93e2aadb43c89c92e89f52b8 | https://github.com/Ismail-Mustapha/tutorials/tree/0ccfbf0047db855e93e2aadb43c89c92e89f52b8 |
MinibatchStd | # 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_... | Tak-jae-ho/RGBD-GAN-pytorch | MinibatchStd | false | 1,128 | [
"MIT"
] | 0 | 4fb1bc1de7b7807fd4f2d346d9b688a2d257eedb | https://github.com/Tak-jae-ho/RGBD-GAN-pytorch/tree/4fb1bc1de7b7807fd4f2d346d9b688a2d257eedb |
GLU | import torch
import torch.nn as nn
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc / 2)
return x[:, :nc] * torch.sigmoid(x[:, nc:])
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SeungyounShin/c3-gan | GLU | false | 14,393 | [
"BSD-2-Clause"
] | 105 | 1fae645674c896b4bcb93e910034519f470a6a96 | https://github.com/SeungyounShin/c3-gan/tree/1fae645674c896b4bcb93e910034519f470a6a96 |
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
from torch.nn import Module
f... | TTomatoZhang/GHGCN | MLP | false | 3,079 | [
"Apache-2.0"
] | 0 | 09a07ff9e29e5889b912ca5feff74bb9308eda55 | https://github.com/TTomatoZhang/GHGCN/tree/09a07ff9e29e5889b912ca5feff74bb9308eda55 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | neuralsyn/self-supervised-relational-reasoning | FocalLoss | false | 16,155 | [
"MIT"
] | 130 | 6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 | https://github.com/neuralsyn/self-supervised-relational-reasoning/tree/6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 |
MultiHeadAttention | import math
import torch
import numpy as np
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, input_dim, embed_dim, val_dim=None, key_dim
=None):
super(MultiHeadAttention, self).__init__()
if val_dim is None:
val_dim = embed_dim // n_heads
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DaehanKim/attention-learn-to-route | MultiHeadAttention | false | 11,358 | [
"MIT"
] | 0 | 9ce4fa9a3a136768f92adf3d1e7d62620442f1b7 | https://github.com/DaehanKim/attention-learn-to-route/tree/9ce4fa9a3a136768f92adf3d1e7d62620442f1b7 |
Gaussian_Kernel_Function | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | LOUEY233/Toward-Mutual-Information | Gaussian_Kernel_Function | false | 9,229 | [
"MIT"
] | 0 | cde9ce5c9920bbc9c6e39dafb61ff1dd0c97772f | https://github.com/LOUEY233/Toward-Mutual-Information/tree/cde9ce5c9920bbc9c6e39dafb61ff1dd0c97772f |
VideoNormalizer | # 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... | glee1228/segment_temporal_context_aggregation | VideoNormalizer | false | 6,746 | [
"Apache-2.0"
] | 1 | e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d | https://github.com/glee1228/segment_temporal_context_aggregation/tree/e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d |
MultiHeadAttentionMemory | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, *, d_model: int, d_k: int, d_v: int, h: int, m: int):
"""
:param d_model: Output dimensio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YehLi/xmodaler | MultiHeadAttentionMemory | false | 14,708 | [
"Apache-2.0"
] | 830 | 5340054398c076cfa717317d151ca595c5e37198 | https://github.com/YehLi/xmodaler/tree/5340054398c076cfa717317d151ca595c5e37198 |
TensorCumsum | import torch
class TensorCumsum(torch.nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input):
return torch.cumsum(input, dim=self.dim)
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... | Minyus/kedex | TensorCumsum | false | 9,676 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
AddBias | import torch
import torch.nn as nn
class AddBias(nn.Module):
def __init__(self, bias):
super(AddBias, self).__init__()
self.bias = bias
def forward(self, x):
return x + self.bias
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'bias... | 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... | ErikML/convex_adversarial | AddBias | false | 401 | [
"MIT"
] | 0 | 52652943cfdb54199b579dbe70d3be20d2a13f23 | https://github.com/ErikML/convex_adversarial/tree/52652943cfdb54199b579dbe70d3be20d2a13f23 |
Concat | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda =... | mcx/ReAgent | Concat | false | 4,108 | [
"BSD-3-Clause"
] | 0 | 57b58a8b3a6b74bb87a197b73a6cd108ddad895e | https://github.com/mcx/ReAgent/tree/57b58a8b3a6b74bb87a197b73a6cd108ddad895e |
Att | # 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... | HCShi/IONet | Att | false | 18,380 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b |
SuperPointNet | import torch
import torch.optim
import torch.utils.data
class SuperPointNet(torch.nn.Module):
""" Pytorch definition of SuperPoint Network. """
def __init__(self):
super(SuperPointNet, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Merical/pytorch-superpoint | SuperPointNet | false | 2,697 | [
"MIT"
] | 0 | b1f6e587b0f68a8a647773e4128b4f504edb4d58 | https://github.com/Merical/pytorch-superpoint/tree/b1f6e587b0f68a8a647773e4128b4f504edb4d58 |
UnStackDelta | # 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... | maxwellzh/CAT | UnStackDelta | false | 16,022 | [
"Apache-2.0"
] | 237 | b1a9c3f95e84d968593a05bf8b176b5f77b8055e | https://github.com/maxwellzh/CAT/tree/b1a9c3f95e84d968593a05bf8b176b5f77b8055e |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 20, kernel_size=4)
self.conv3 = nn.Conv2d(20, 20, kernel_size=2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Prabhu204/MNISTdata | Net | false | 9,381 | [
"MIT"
] | 0 | 1ab3be23a0cec8caacd4adec6cd3c413639a62cc | https://github.com/Prabhu204/MNISTdata/tree/1ab3be23a0cec8caacd4adec6cd3c413639a62cc |
AdaptiveCatAvgMaxPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | BarneyQiao/CondenseNetV2 | AdaptiveCatAvgMaxPool2d | false | 13,814 | [
"MIT"
] | 80 | c771957cb8fe466d0ecbafe9060e4c342a33fc4d | https://github.com/BarneyQiao/CondenseNetV2/tree/c771957cb8fe466d0ecbafe9060e4c342a33fc4d |
Upsample | # 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... | Jackson-Kang/VQVC-Pytorch | Upsample | false | 8,331 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
new_class | import torch
from torch import nn
class new_class(nn.Module):
def __init__(self):
super(new_class, self).__init__()
def forward(self, input):
return input + 1
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | AluminiumOxide/pytorch_base_-tutorial | new_class | false | 1,931 | [
"Apache-2.0"
] | 0 | a6d3bea6070c7c774dcd7c55d94b0a1441548c8b | https://github.com/AluminiumOxide/pytorch_base_-tutorial/tree/a6d3bea6070c7c774dcd7c55d94b0a1441548c8b |
Anchor3DHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
import torch.utils.dlpack
assert_size_s... | Jaein94/Open3D-ML | Anchor3DHead | false | 9,359 | [
"MIT"
] | 0 | 815c111229322d562e11ea3148ad6568ccf13d1d | https://github.com/Jaein94/Open3D-ML/tree/815c111229322d562e11ea3148ad6568ccf13d1d |
HighwayNetwork | # 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_... | Rongjiehuang/Multiband-WaveRNN | HighwayNetwork | false | 8,726 | [
"MIT"
] | 18 | 432e449678220eed841fcb4971415e2e0ac4d9bb | https://github.com/Rongjiehuang/Multiband-WaveRNN/tree/432e449678220eed841fcb4971415e2e0ac4d9bb |
CONV | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class CONV(nn.Module):
def __init__(self, input_shape, device):
super(CONV, self).__init__()
self.device = device
self.input_shape = input_shape
self.poolavg = nn.AvgPool2d(2, 2)
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | pupupue/Deep-RL-atari | CONV | false | 7,513 | [
"MIT"
] | 1 | 9b97157f87826feafcf272761d7eef9693a2b2c4 | https://github.com/pupupue/Deep-RL-atari/tree/9b97157f87826feafcf272761d7eef9693a2b2c4 |
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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Dora-The-Kid/culture_network | Discriminator | false | 2,182 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
EncoderLayer | # 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.... | muberraozmen/MrMP | EncoderLayer | false | 4,057 | [
"MIT"
] | 0 | da6bcccbad85a682c848ff4aa1121c773d779e57 | https://github.com/muberraozmen/MrMP/tree/da6bcccbad85a682c848ff4aa1121c773d779e57 |
TreeMaxPool | import torch
from torch import nn
import torch.utils.data
class TreeMaxPool(nn.Module):
def forward(self, trees):
return trees[0].max(dim=2).values
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | balsa-project/balsa | TreeMaxPool | false | 3,159 | [
"Apache-2.0"
] | 0 | 36f3fb35d33589928d761b89de52367d18d08fd8 | https://github.com/balsa-project/balsa/tree/36f3fb35d33589928d761b89de52367d18d08fd8 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | linsats/GRAC2.0 | Actor | false | 3,924 | [
"MIT"
] | 0 | 2fde25103b2316a3435ef0ebdbf471ec4e204fbe | https://github.com/linsats/GRAC2.0/tree/2fde25103b2316a3435ef0ebdbf471ec4e204fbe |
Attention | import math
import torch
import torch.nn as nn
import torch as t
class Linear(nn.Module):
"""
Linear Module
"""
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
"""
:param in_dim: dimension of input
:param out_dim: dimension of output
:param bias: boole... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Munna-Manoj/Team7_TTS | Attention | false | 11,741 | [
"MIT"
] | 0 | 5e2d473a2afe429023876bcc51c2ac966a4938b8 | https://github.com/Munna-Manoj/Team7_TTS/tree/5e2d473a2afe429023876bcc51c2ac966a4938b8 |
BertPooler | # 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... | ArrowLuo/GRACE | BertPooler | false | 8,775 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YiwenShaoStephen/snowfall | TransformerEncoderLayer | false | 14,704 | [
"Apache-2.0"
] | 145 | 949226f35b29c629cb03cae36fa43da5993d27a3 | https://github.com/YiwenShaoStephen/snowfall/tree/949226f35b29c629cb03cae36fa43da5993d27a3 |
L1Loss | import torch
from torch import nn
from torch import torch
class L1Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, Yp, Yt):
num = Yt.size(0)
Yp = Yp.view(num, -1)
Yt = Yt.view(num, -1)
loss = nn.functional.l1_loss(Yp, Yt)
return 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
from torch import nn
f... | oskarnatan/RGBDVS-fusion | L1Loss | false | 7,423 | [
"MIT"
] | 1 | 5e560f54442d387a86e3a469107cf65859693987 | https://github.com/oskarnatan/RGBDVS-fusion/tree/5e560f54442d387a86e3a469107cf65859693987 |
Coboundary | import torch
import torch.nn as nn
import torch.nn.functional
class Coboundary(nn.Module):
def __init__(self, C_in, C_out, enable_bias=True, variance=1.0):
super().__init__()
assert C_in > 0
assert C_out > 0
self.C_in = C_in
self.C_out = C_out
self.enable_bias = en... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._... | AtreusCorp/simplicial_neural_networks | Coboundary | false | 8,862 | [
"MIT"
] | 0 | 7a903dd02494811ace0d86e36476059e156fc15c | https://github.com/AtreusCorp/simplicial_neural_networks/tree/7a903dd02494811ace0d86e36476059e156fc15c |
DocUnetLoss | # 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
... | hologerry/DewarpNet | DocUnetLoss | false | 3,611 | [
"MIT"
] | 0 | b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 | https://github.com/hologerry/DewarpNet/tree/b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 |
PNTrainingSigmoid | # 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... | mxuq/Imbalance-PU | PNTrainingSigmoid | false | 7,307 | [
"MIT"
] | 1 | fd4403b05f98ca6bc8156783e8275888d63f6435 | https://github.com/mxuq/Imbalance-PU/tree/fd4403b05f98ca6bc8156783e8275888d63f6435 |
MaxPool2dDynamicSamePadding | import math
import torch
from torch import nn
from torch.nn import functional as F
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
def __init__(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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Kwongy/Pretrained-backbone-Pytorch | MaxPool2dDynamicSamePadding | false | 2,478 | [
"MIT"
] | 0 | 1b24bb677e0fd420cce32715c1ead8f0c804d707 | https://github.com/Kwongy/Pretrained-backbone-Pytorch/tree/1b24bb677e0fd420cce32715c1ead8f0c804d707 |
DepthwiseSeperableConv1d | # 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... | allenye0119/pytorch-modules | DepthwiseSeperableConv1d | false | 12,074 | [
"MIT"
] | 0 | c7683ef63478becca3b79a7498840450da33f468 | https://github.com/allenye0119/pytorch-modules/tree/c7683ef63478becca3b79a7498840450da33f468 |
LinearExcitability | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in_features)`
- 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
import math
from torch import nn
from torch.nn.parameter import Parameter
assert... | JosephKJ/continual-learning | LinearExcitability | false | 9,165 | [
"MIT"
] | 0 | 2e526cc58ab35d76cddc1df46ee421baea89a727 | https://github.com/JosephKJ/continual-learning/tree/2e526cc58ab35d76cddc1df46ee421baea89a727 |
GeneralizedMeanPooling | import torch
from torch import Tensor
import torch.nn as nn
from torch.functional import Tensor
import torch.nn.functional as F
from torch import Tensor
from torch.nn.parameter import Parameter
def gem(x: 'Tensor', p: 'Parameter', eps: 'float'=1e-06, clamp=True) ->Tensor:
if clamp:
x = x.clamp(min=eps)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | colinski/mmclassification | GeneralizedMeanPooling | false | 6,468 | [
"Apache-2.0"
] | 1 | 447c8291bc2e2abda6f3eafe2e6d0f13d65843cb | https://github.com/colinski/mmclassification/tree/447c8291bc2e2abda6f3eafe2e6d0f13d65843cb |
GramMatrix | # 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 torchvision.datasets import *
import torch.nn as nn
from torchvision.transf... | JJavierga/PyTorch-Encoding | GramMatrix | false | 9,470 | [
"MIT"
] | 0 | 207254b2a60276a31ffa24b76ae84df27c6ebf94 | https://github.com/JJavierga/PyTorch-Encoding/tree/207254b2a60276a31ffa24b76ae84df27c6ebf94 |
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.... | etaoxing/crl_alfred | SelfAttn | false | 15,314 | [
"MIT"
] | 148 | cad500cf84f71e47f1191e7810dde0c74d295f08 | https://github.com/etaoxing/crl_alfred/tree/cad500cf84f71e47f1191e7810dde0c74d295f08 |
SimpleArgSortModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleArgSortModule(torch.nn.Module):
def __init__(self, descending=True):
super(SimpleArgSortModule, self).__init__()
self.descending = descending
def forward(self, inputs):
return torch.argsort(inputs, dim=-1, de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | mciprian13/glow | SimpleArgSortModule | false | 3,994 | [
"Apache-2.0"
] | 0 | 90f88205d9bf8baff8df5bbda51c9d138e3e668b | https://github.com/mciprian13/glow/tree/90f88205d9bf8baff8df5bbda51c9d138e3e668b |
Decoder2 | import torch
import torch.nn as nn
class Decoder2(nn.Module):
def __init__(self, model=None, fixed=False):
super(Decoder2, self).__init__()
self.fixed = fixed
self.conv21 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv12 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1)
self.conv11 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EndyWon/Texture-Reformer | Decoder2 | false | 8,133 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
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
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | PeterouZh/CIPS-3D | EqualLinear | false | 14,175 | [
"MIT"
] | 308 | 9b8bfa0fb23f642af042e150ccd70408f9d137c6 | https://github.com/PeterouZh/CIPS-3D/tree/9b8bfa0fb23f642af042e150ccd70408f9d137c6 |
InnerProductLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | dreaming-qin/RecBole | InnerProductLoss | false | 12,311 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
HypergradTransform | import torch
class HypergradTransform(torch.nn.Module):
"""Hypergradient-style per-parameter learning rates"""
def __init__(self, param, lr=0.01):
super(HypergradTransform, self).__init__()
self.lr = lr * torch.ones_like(param, requires_grad=True)
self.lr = torch.nn.Parameter(self.lr)... | 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... | Brikwerk/learn2learn | HypergradTransform | false | 13,423 | [
"MIT"
] | 1,774 | 7997c13c26ec627d13ce77ba98427260df78ada8 | https://github.com/Brikwerk/learn2learn/tree/7997c13c26ec627d13ce77ba98427260df78ada8 |
Dice | # 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_... | SUSTechBruce/RL_CTR | Dice | false | 2,794 | [
"Apache-2.0"
] | 0 | 817398dc1c117e22f41281830ae3c33bba8062d3 | https://github.com/SUSTechBruce/RL_CTR/tree/817398dc1c117e22f41281830ae3c33bba8062d3 |
GraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Qin-J/Multi-site-transfer-classification-of-major-depressive-disorder | GraphConv | false | 2,744 | [
"Apache-2.0"
] | 0 | f6af292388ec83a9851a2254f38e8d90adfe4e6c | https://github.com/Qin-J/Multi-site-transfer-classification-of-major-depressive-disorder/tree/f6af292388ec83a9851a2254f38e8d90adfe4e6c |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | pkashinkunti/retinanet-examples | FocalLoss | false | 7,469 | [
"BSD-3-Clause"
] | 1 | 30466522c018d4d0bd921485024e871b32ec4e73 | https://github.com/pkashinkunti/retinanet-examples/tree/30466522c018d4d0bd921485024e871b32ec4e73 |
MaxPool2d | import torch
from typing import *
from torch import nn
class MaxPool2d(nn.Module):
def __init__(self, kernel_size, **kwargs):
super().__init__()
stride = kwargs.setdefault('stride', kernel_size)
padding = kwargs.setdefault('padding', 0)
dilation = kwargs.setdefault('dilation', 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 typing import *
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | cbarrick/csb | MaxPool2d | false | 6,395 | [
"MIT"
] | 1 | 0368036ddb7594c0b6e7cdc704aeec918786e58a | https://github.com/cbarrick/csb/tree/0368036ddb7594c0b6e7cdc704aeec918786e58a |
Sub | # 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... | bunderhi/torch2trt | Sub | false | 1,610 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
CosSim | import torch
import torch.nn as nn
class CosSim(nn.Module):
def __init__(self, nfeat, nclass, codebook=None, learn_cent=True):
super(CosSim, self).__init__()
self.nfeat = nfeat
self.nclass = nclass
self.learn_cent = learn_cent
if codebook is None:
codebook = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | gajrajgchouhan/orthohash | CosSim | false | 15,404 | [
"BSD-3-Clause"
] | 51 | 4e04cfe1dd32e21ba004e308d5a1ce9c8578ea2b | https://github.com/gajrajgchouhan/orthohash/tree/4e04cfe1dd32e21ba004e308d5a1ce9c8578ea2b |
StyledConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Liamkuo/SAIR | StyledConv | false | 17,584 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
SSP | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def ssp(*args, **kwargs):
return F.softplus(*args, **kwargs) - np.log(2)
class SSP(nn.Softplus):
def forward(self, xs):
return ssp(xs, self.beta, self.threshold)
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch import nn
import torch.nn.functi... | PKUfjh/deepqmc | SSP | false | 14,138 | [
"MIT"
] | 224 | 2a948ce712dd4e40568aa35931527e6c874eba73 | https://github.com/PKUfjh/deepqmc/tree/2a948ce712dd4e40568aa35931527e6c874eba73 |
ResizeConv2d | import torch
from torch import nn
import torch.nn.functional as F
class ResizeConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, scale_factor,
mode='nearest'):
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
self.conv = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | neuronphysics/FEAIML | ResizeConv2d | false | 10,626 | [
"MIT"
] | 0 | a31ae0d9f526f489fca1ca4b01dd8f06115de450 | https://github.com/neuronphysics/FEAIML/tree/a31ae0d9f526f489fca1ca4b01dd8f06115de450 |
SinusoidRelativePositionalEmbedding | import torch
import torch.nn as nn
class SinusoidRelativePositionalEmbedding(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len))
pos = (pos - pos.unsqueeze(-1)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | yzhangcs/parser | SinusoidRelativePositionalEmbedding | false | 16,791 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a |
EquiangularAvgUnpool | # 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | phil-hawkins/deepsphere-pytorch | EquiangularAvgUnpool | false | 16,244 | [
"MIT"
] | 99 | f23c531445b3ddf234c7e98cdadb010163051e6d | https://github.com/phil-hawkins/deepsphere-pytorch/tree/f23c531445b3ddf234c7e98cdadb010163051e6d |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
"""
MLP
"""
def __init__(self, hidden_layers, input_size, output_size, seed=1):
"""
`hidden_layers`: list, the number of neurons for every layer;
`input_size`: number of states;
`output_size`: number of actions;
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ILABUTK/MLePOMDP_Early_Sepsis_Detection | MLP | false | 11,503 | [
"MIT"
] | 0 | 7e6fdb1e425ee3cd5aa4142287c1e7dba28a126f | https://github.com/ILABUTK/MLePOMDP_Early_Sepsis_Detection/tree/7e6fdb1e425ee3cd5aa4142287c1e7dba28a126f |
OutputGenerator | # 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.... | guyjacoby/original-transformer-pytorch | OutputGenerator | false | 3,562 | [
"MIT"
] | 0 | 19e9ab4af3f0ee1ca81f6436eb18c36382bfbc1d | https://github.com/guyjacoby/original-transformer-pytorch/tree/19e9ab4af3f0ee1ca81f6436eb18c36382bfbc1d |
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... | PoCInnovation/Koic | AconC | false | 8,679 | [
"MIT"
] | 13 | eca53b53b7242c1e83213ef9408366ca0a346358 | https://github.com/PoCInnovation/Koic/tree/eca53b53b7242c1e83213ef9408366ca0a346358 |
HighwayMLP | # 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.... | TimSYQQX/glyce | HighwayMLP | false | 14,511 | [
"Apache-2.0"
] | 396 | 1542ed30ce104c25aa5c69ffcc9cc5ef2fcda975 | https://github.com/TimSYQQX/glyce/tree/1542ed30ce104c25aa5c69ffcc9cc5ef2fcda975 |
AsymmetricLossOptimized | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(sel... | 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... | davidaderup/query2labels | AsymmetricLossOptimized | false | 15,124 | [
"MIT"
] | 164 | 5a10c861dda85d94ba01ec6ad4119eef67a9f441 | https://github.com/davidaderup/query2labels/tree/5a10c861dda85d94ba01ec6ad4119eef67a9f441 |
ExtendedModel | import torch
import torch.nn as nn
class ExtendedModel(nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(ExtendedModel, self).__init__()
self.linear1 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | sauyon/BentoML | ExtendedModel | false | 4,282 | [
"Apache-2.0"
] | 0 | ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 | https://github.com/sauyon/BentoML/tree/ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 |
BiaffineScorer | # 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
import torch.cuda
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | KaijuML/dtt-multi-branch | BiaffineScorer | false | 17,528 | [
"Apache-2.0"
] | 8 | a49850a95034e58d387b9d48c647cfc2b83c45b5 | https://github.com/KaijuML/dtt-multi-branch/tree/a49850a95034e58d387b9d48c647cfc2b83c45b5 |
Cast | import torch
import torch.onnx
import torch.nn as nn
class Cast(nn.Module):
def forward(self, x):
return x.type(torch.int32)
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
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | Cast | false | 16,069 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
GAT | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=False
):
super(GraphAt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | StrangeTcy/Q-BERT | GAT | false | 14,465 | [
"MIT"
] | 57 | 4e4cd4ddda3036d4bf7d878641592462189245d4 | https://github.com/StrangeTcy/Q-BERT/tree/4e4cd4ddda3036d4bf7d878641592462189245d4 |
SchedulerTestNet | import torch
from torch.nn import functional as F
class SchedulerTestNet(torch.nn.Module):
"""
adapted from: https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
"""
def __init__(self):
super(SchedulerTestNet, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 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... | Luab/pytorch-lightning-bolts | SchedulerTestNet | false | 11,714 | [
"Apache-2.0"
] | 0 | b8ac85154465956b06fd1005b21b071af5493f11 | https://github.com/Luab/pytorch-lightning-bolts/tree/b8ac85154465956b06fd1005b21b071af5493f11 |
SpatialMaxPool | import torch
import torch.nn as nn
class SpatialMaxPool(nn.Module):
"""
Performs max pooling over spatial dimensions; keeps only the first `ndim`
dimensions of the input.
"""
def __init__(self, ndim=2):
super(SpatialMaxPool, self).__init__()
self.ndim = ndim
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CPJKU/kagglebirds2020 | SpatialMaxPool | false | 17,046 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
conv_embedding | import torch
from torch import nn
class conv_embedding(nn.Module):
def __init__(self, in_channels, out_channels, patch_size, stride, padding):
super(conv_embedding, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=
patch_size, stride=stride, padding=paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | naver-ai/PfLayer | conv_embedding | false | 16,138 | [
"Apache-2.0"
] | 59 | da8f80b2ea3b6bd7fbee3beee8b1516c89bc0441 | https://github.com/naver-ai/PfLayer/tree/da8f80b2ea3b6bd7fbee3beee8b1516c89bc0441 |
_Linear | # 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.... | CoAxLab/newremagine | _Linear | false | 8,909 | [
"MIT"
] | 0 | 5ae1c579121c93271ebf5dcef45bd66e8daea3a7 | https://github.com/CoAxLab/newremagine/tree/5ae1c579121c93271ebf5dcef45bd66e8daea3a7 |
ContrastivePairwiseEmbeddingLoss | import torch
from torch.nn.modules.loss import *
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import *
from torch.optim import *
from torch.optim.lr_scheduler import *
class ContrastivePairwiseEmbeddingLoss(nn.Module):
"""
ContrastivePairwiseEmbeddingLoss – proof of concept criterion.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | pokidyshev/catalyst | ContrastivePairwiseEmbeddingLoss | false | 16,264 | [
"Apache-2.0"
] | 46 | bfe2cc2af7b02bd954fb0b4a0cae8b350f56789a | https://github.com/pokidyshev/catalyst/tree/bfe2cc2af7b02bd954fb0b4a0cae8b350f56789a |
PreNet | import torch
from torch import nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | KonstantinPakulev/OSM-one-shot-multispeaker | PreNet | false | 2,456 | [
"MIT"
] | 0 | 5cee1b6cb7dc7a3b4b24171340855a42824925f7 | https://github.com/KonstantinPakulev/OSM-one-shot-multispeaker/tree/5cee1b6cb7dc7a3b4b24171340855a42824925f7 |
AccidentPredictor | # 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_... | GIST-railab/UString | AccidentPredictor | false | 8,136 | [
"MIT"
] | 30 | 490a6b0b29fbf434e094717fe272f78bc5d34956 | https://github.com/GIST-railab/UString/tree/490a6b0b29fbf434e094717fe272f78bc5d34956 |
SpacialGatingUnit | # 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... | Aarsh2001/annotated_deep_learning_paper_implementations | SpacialGatingUnit | false | 4,782 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
EqualLinearActModule | # 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 copy import deepcopy
from functools import partial
fr... | akimotty877/mmediting | EqualLinearActModule | false | 3,068 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 |
AdaptiveCatAvgMaxPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
asser... | Fanzhongjie/ARFE | AdaptiveCatAvgMaxPool2d | false | 431 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
SeparableConv1D | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class SeparableConv1D(nn.Module):
"""This class implements separable convolution, i.e. a depthwise and a pointwise layer"""
def __init__(self, config, input_filters, output_filters, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C... | Clemens123/transformers | SeparableConv1D | false | 12,847 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | timgianitsos/squad | ScaledDotProductAttention | false | 13,186 | [
"MIT"
] | 0 | 6ab502652e3528cfeeddfb8eba05221443a35294 | https://github.com/timgianitsos/squad/tree/6ab502652e3528cfeeddfb8eba05221443a35294 |
DenseModelV3 | import torch
import torch.nn as nn
class DenseModelV3(nn.Module):
def __init__(self, input_dim, num_classes=2):
super(DenseModelV3, self).__init__()
self.fc1 = nn.Linear(input_dim, 2000)
self.relu1 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(2000, 2000)
self.relu2 = nn.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 import triton_helpers
import torch.nn as nn
assert_... | chawins/adv-exp | DenseModelV3 | false | 6,447 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
Recover_from_density | # 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... | CastleLiang/UrbanFM | Recover_from_density | false | 4,964 | [
"MIT"
] | 1 | fb3aff0828099bff31032dc26748d758113af892 | https://github.com/CastleLiang/UrbanFM/tree/fb3aff0828099bff31032dc26748d758113af892 |
SmallDecoder4_16x | # 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.... | MingSun-Tse/pytorch-AdaIN | SmallDecoder4_16x | false | 2,670 | [
"MIT"
] | 0 | 02ae320345232983c754ea233613aedc21e4d348 | https://github.com/MingSun-Tse/pytorch-AdaIN/tree/02ae320345232983c754ea233613aedc21e4d348 |
MuSigmaEncoder | # 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_... | TheBonheurs/neural-processes | MuSigmaEncoder | false | 9,556 | [
"MIT"
] | 0 | 5834bc65f406456e53c363ade1cb0f2a5f23a033 | https://github.com/TheBonheurs/neural-processes/tree/5834bc65f406456e53c363ade1cb0f2a5f23a033 |
ISAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | ISAB | false | 4,910 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
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.nn import Module
i... | ShivangMathur1/Small-Pytorch-Projects | Autoencoder | false | 1,895 | [
"MIT"
] | 0 | aebc6e81103fe2a6830caeedc1b17227e211a6e5 | https://github.com/ShivangMathur1/Small-Pytorch-Projects/tree/aebc6e81103fe2a6830caeedc1b17227e211a6e5 |
SmoothL1Loss | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
import torch.multiprocessing
class SmoothL1Loss(nn.Module):
"""Smooth L1 Loss"""
def __init__(self, beta=0.11):
super().__init__()
self.beta = beta
def forward(self, pred, target):
x = (pred - target).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
import torch.nn as nn
import torch.cuda
import torch.distributed
import t... | azuredsky/retinanet-examples | SmoothL1Loss | false | 9,797 | [
"BSD-3-Clause"
] | 0 | 1b35d8e7d3360050f25fd80e09ecac3eb2654301 | https://github.com/azuredsky/retinanet-examples/tree/1b35d8e7d3360050f25fd80e09ecac3eb2654301 |
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.... | Ahren09/RecBole | MultiHeadAttention | false | 1,945 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
GeLU | import torch
import torch.nn as nn
class GeLU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 +
0.044715 * x * x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | IamHimon/re2 | GeLU | false | 11,502 | [
"Apache-2.0"
] | 0 | d16b0ffc385f7b118a6160d035250da8d6320534 | https://github.com/IamHimon/re2/tree/d16b0ffc385f7b118a6160d035250da8d6320534 |
AE_big_2D_v1 | # 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 ... | gitter-badger/HEPAutoencoders | AE_big_2D_v1 | false | 12,454 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
StyleBlock | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
from typing import Optional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-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.triton_helpers import libdevice
import math
import ... | Hadryan/nn | StyleBlock | false | 9,389 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
RobertaClassificationHead | # 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 ... | Amber-Chaeeunk/Open-Domain-Question-Answering | RobertaClassificationHead | false | 18,061 | [
"MIT"
] | 5 | 725e369a4409c54bf11bcfb9db53865d8fc1f935 | https://github.com/Amber-Chaeeunk/Open-Domain-Question-Answering/tree/725e369a4409c54bf11bcfb9db53865d8fc1f935 |
UIAttention | # 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.... | Hui-Li/MCRec_PyTorch | UIAttention | false | 17,393 | [
"MIT"
] | 9 | da4da77d2cade40c0a1961481c8e47ac396d12ee | https://github.com/Hui-Li/MCRec_PyTorch/tree/da4da77d2cade40c0a1961481c8e47ac396d12ee |
SimpleCNN | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self):
pass
class SimpleCNN(Model):
def __init__(self):
super(Model, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=64,
kernel_size=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... | Cuilie/Collect-feature-maps | SimpleCNN | false | 5,078 | [
"MIT"
] | 1 | 32e8ac59690837f2a299ab6d4c11b98f5d3d721a | https://github.com/Cuilie/Collect-feature-maps/tree/32e8ac59690837f2a299ab6d4c11b98f5d3d721a |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SeungoneKim/Transformer_implementation | ScaledDotProductAttention | false | 1,055 | [
"Apache-2.0"
] | 0 | a52bf552eb645fc9bfb812cc26842fc147d6c008 | https://github.com/SeungoneKim/Transformer_implementation/tree/a52bf552eb645fc9bfb812cc26842fc147d6c008 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.a1 = nn.Conv2d(5, 16, kernel_size=3, padding=1)
self.a2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
self.a3 = nn.Conv2d(16, 32, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | blockide/Chess-ML | Net | false | 12,198 | [
"MIT"
] | 0 | 3b1572f715ed710f5ce240c76bb79ae8f186f32a | https://github.com/blockide/Chess-ML/tree/3b1572f715ed710f5ce240c76bb79ae8f186f32a |
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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dyna... | yifding/W2NER | LayerNorm | false | 13,144 | [
"MIT"
] | 0 | d13128e45f3930a8b8faa794318939dc90a75974 | https://github.com/yifding/W2NER/tree/d13128e45f3930a8b8faa794318939dc90a75974 |
ColorJitterLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | yingnengd/MyGAN | ColorJitterLayer | false | 4,745 | [
"MIT"
] | 0 | 6e4abbe165c8f3b1e1b69d5d01177712761a3a1c | https://github.com/yingnengd/MyGAN/tree/6e4abbe165c8f3b1e1b69d5d01177712761a3a1c |
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):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | adriaciurana/adriaciurana-udacity-project-2 | Critic | false | 12,050 | [
"MIT"
] | 0 | a0af7086df586b537cd10a880f1d354240ff31a5 | https://github.com/adriaciurana/adriaciurana-udacity-project-2/tree/a0af7086df586b537cd10a880f1d354240ff31a5 |
Align | import torch
import torch.nn.functional as F
class Align(torch.nn.Module):
def __init__(self, p):
super(Align, self).__init__()
self.p = p
def forward(self, e1, e2):
pred = -torch.norm(e1 - e2, p=self.p, dim=1)
return pred
def only_pos_loss(self, e1, r, e2):
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.as... | weihangzhang/EAkit | Align | false | 16,695 | [
"MIT"
] | 102 | dde8e914480cd1a3585271f70db11d567d9c2a04 | https://github.com/weihangzhang/EAkit/tree/dde8e914480cd1a3585271f70db11d567d9c2a04 |
Scaler | # 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kvenkman/hummingbird | Scaler | false | 3,860 | [
"MIT"
] | 0 | dac08f4ff4a4103df4a8e83329a02f2d804bf34d | https://github.com/kvenkman/hummingbird/tree/dac08f4ff4a4103df4a8e83329a02f2d804bf34d |
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