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 |
|---|---|---|---|---|---|---|---|---|---|---|
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
self).__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 11,829 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
SVDBilinear | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class SVDBilinear(nn.Module):
"""
my bilinear matmul but reducing parameter dimension using peusodu-SVD
"""
def __init__(self, num_basis, in1_features, in2_features, out_features):
supe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.init as init
assert_size_strid... | Yindong-Zhang/myGAT | SVDBilinear | false | 18,156 | [
"MIT"
] | 6 | f69132f21785d3a6bf1ec014890adeb124c89e8d | https://github.com/Yindong-Zhang/myGAT/tree/f69132f21785d3a6bf1ec014890adeb124c89e8d |
Attention | # 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.... | RaleLee/conv-emotion | Attention | false | 11,802 | [
"MIT"
] | 0 | 1b07223cbdfd52eb31e913e982d18ff1ed3daf08 | https://github.com/RaleLee/conv-emotion/tree/1b07223cbdfd52eb31e913e982d18ff1ed3daf08 |
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
import torch.utils.data
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | Bhaskers-Blu-Org1/gfmn | Upsample | false | 7,756 | [
"Apache-2.0"
] | 15 | 52b4fd005f8c52297bd6aa5d93e4a1c8d46f56ce | https://github.com/Bhaskers-Blu-Org1/gfmn/tree/52b4fd005f8c52297bd6aa5d93e4a1c8d46f56ce |
ConvSwishInplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishInplace, self).__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSwishInplace | false | 9,719 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_str... | BradLin0819/kg2text | ContextGate | false | 13,408 | [
"Apache-2.0"
] | 86 | e586eb2027c0d85db9826cbe1d9e14f2d26fc93f | https://github.com/BradLin0819/kg2text/tree/e586eb2027c0d85db9826cbe1d9e14f2d26fc93f |
Logits | # 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._utils
from itertools import product as product
import... | Capetian/FaceX-Zoo | Logits | false | 4,963 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
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... | David-19940718/mmclassification | FocalLoss | false | 5,052 | [
"Apache-2.0"
] | 1 | 987dd45457e38c4787237ea468799849dce11ada | https://github.com/David-19940718/mmclassification/tree/987dd45457e38c4787237ea468799849dce11ada |
unrotate | # 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... | amonod/udvd | unrotate | false | 1,425 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
AE_2D_v5 | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D_v5(nn.Module):
def __init__(self, n_features=4):
super(AE_2D_v5, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_2D_v5 | false | 12,449 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
GRULRCell | # 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 ... | ShishirPatil/EdgeML-1 | GRULRCell | false | 1,073 | [
"MIT"
] | 0 | cbba9f8b989e545788427c004eb8450e7e4c1a21 | https://github.com/ShishirPatil/EdgeML-1/tree/cbba9f8b989e545788427c004eb8450e7e4c1a21 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
d... | 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
from itertools import product as product
import torch.utils.data.distributed
assert_size_stride = ... | Capetian/FaceX-Zoo | GlobalAvgPool2d | false | 4,957 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
ClsHead | # 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.... | eminem171333491/PaddleOCR2Pytorch | ClsHead | false | 3,457 | [
"Apache-2.0"
] | 0 | ec466bb3a689eccb9290e9f80812a45301d3b030 | https://github.com/eminem171333491/PaddleOCR2Pytorch/tree/ec466bb3a689eccb9290e9f80812a45301d3b030 |
GaussActivation | import torch
from torch import nn
from torch.nn.parameter import Parameter
class GaussActivation(nn.Module):
def __init__(self, a, mu, sigma1, sigma2):
super(GaussActivation, self).__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dty... | 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... | DLwbm123/LBAM_inpainting | GaussActivation | false | 17,197 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
GMoF_unscaled | # 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... | sanweiliti/HMP | GMoF_unscaled | false | 16,374 | [
"MIT"
] | 92 | 3d1a96ec86a72396349daa9f8dde9b2e5a3fc578 | https://github.com/sanweiliti/HMP/tree/3d1a96ec86a72396349daa9f8dde9b2e5a3fc578 |
Unit3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class Unit3D(nn.Module):
def __init__(self, in_channels, output_channels, kernel_shape=(1, 1, 1),
stride=(1, 1, 1), padding='spatial_valid', activation_fn=F.relu,
use_batch_norm=False, use_bias=False):
"""Initializes Unit3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Cogito2012/OpenTAL | Unit3D | false | 7,897 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
CharbonnierLoss | # 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... | IndigoPurple/EFENet | CharbonnierLoss | false | 8,293 | [
"MIT"
] | 11 | e88234486f19534274a0a20badc251788ac67e31 | https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31 |
InnerProductNetwork | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | JazonJiao/pytorch-fm | InnerProductNetwork | false | 13,869 | [
"MIT"
] | 734 | 7192e7861fa54341d5b2df995f92858f583ea09e | https://github.com/JazonJiao/pytorch-fm/tree/7192e7861fa54341d5b2df995f92858f583ea09e |
NoiseLayer | import torch
import torch.nn as nn
class NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(s... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | justinpinkney/ganspace | NoiseLayer | false | 10,459 | [
"Apache-2.0"
] | 0 | 7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | https://github.com/justinpinkney/ganspace/tree/7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f |
TimeEncode | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | amazon-research/tgl | TimeEncode | false | 18,298 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 |
EquivariantLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
from torch.nn.modules.batchnorm import _BatchNorm
class MyBatchNorm1d(_BatchNorm):
"""Applies Batch Normalization over a 2d or 3d input that is seen as a
mini-batch.
.. math::
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Manojbhat09/Sane-annotation-shape-complete | EquivariantLayer | false | 17,700 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
BVNet | # 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.... | SpyrosMouselinos/DeltaFormers | BVNet | false | 5,893 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
ATLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
"""
Module for calculating AT Loss
:param norm_type (int): Norm to be used in calculating loss
"""
def __init__(self, norm_type=2):
super(ATLoss, self).__init__()
self.p = norm_type
... | 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
import... | DA-southampton/KD_Lib | ATLoss | false | 5,057 | [
"MIT"
] | 1 | bd4a9b93b9674607ecf467d280d5cab1c516bdc6 | https://github.com/DA-southampton/KD_Lib/tree/bd4a9b93b9674607ecf467d280d5cab1c516bdc6 |
moving_avg | import math
import torch
import torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(ker... | 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... | MAZiqing/FEDformer | moving_avg | false | 17,649 | [
"MIT"
] | 7 | 7914d39df829494a8172afb9676982c3789d491d | https://github.com/MAZiqing/FEDformer/tree/7914d39df829494a8172afb9676982c3789d491d |
Merge | import torch
import torch.nn as nn
import torch.optim
class Merge(nn.Module):
def __init__(self, hidden_size, embedding_size, dropout=0.5):
super(Merge, self).__init__()
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.em_dropout = nn.Dropout(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.triton_helpers import libdevice
import torch.nn as ... | Myeongchan-Kim/SVAMP | Merge | false | 5,623 | [
"MIT"
] | 1 | 9ff9ad471a61aa390199df4b99beb3b654f5c943 | https://github.com/Myeongchan-Kim/SVAMP/tree/9ff9ad471a61aa390199df4b99beb3b654f5c943 |
DQN_Simple | # 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.autograd import Variable
import torch.nn.functional as F
... | exe1023/GA-final | DQN_Simple | false | 10,174 | [
"MIT"
] | 0 | dad84cda665ef24e9568a79a2e7ff0a00edf5851 | https://github.com/exe1023/GA-final/tree/dad84cda665ef24e9568a79a2e7ff0a00edf5851 |
RWKV_ChannelMix | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn import functional as F
class RWKV_ChannelMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.layer_id = layer_id
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | JunnYu/Paddle-AI-Writer | RWKV_ChannelMix | false | 8,795 | [
"BSD-3-Clause"
] | 25 | 8d211f9e60aeed323b6330065668f54350514c70 | https://github.com/JunnYu/Paddle-AI-Writer/tree/8d211f9e60aeed323b6330065668f54350514c70 |
PredictionConvolutions | import torch
from torch import nn
import torch.optim
import torch.utils.data
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicted as encoded offsets w.r.t each of the 245... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.optim
import torch.utils.data
assert_size_stri... | doduythao/ssd | PredictionConvolutions | false | 12,633 | [
"MIT"
] | 0 | 170064a3edef05d3274b08ea7f622eb3238b5c5c | https://github.com/doduythao/ssd/tree/170064a3edef05d3274b08ea7f622eb3238b5c5c |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | MMorafah/FLIS | ConvBlock | false | 801 | [
"MIT"
] | 0 | 7c93ea7498b98f552ed24331eb0dfcc1f9dcacb0 | https://github.com/MMorafah/FLIS/tree/7c93ea7498b98f552ed24331eb0dfcc1f9dcacb0 |
MiniBatchStddevLayer | # 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.distributed as dist
import torch.autograd as... | jiangwenj02/mmgeneration | MiniBatchStddevLayer | false | 12,610 | [
"Apache-2.0"
] | 0 | da9ad377ae19260467fc332ddb88f505c38a915a | https://github.com/jiangwenj02/mmgeneration/tree/da9ad377ae19260467fc332ddb88f505c38a915a |
LinearEstimator | # 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... | MLIA/LEADS | LinearEstimator | false | 17,650 | [
"MIT"
] | 6 | 4010f6b6e6a56ee049b4b4a9aec1c24b34730616 | https://github.com/MLIA/LEADS/tree/4010f6b6e6a56ee049b4b4a9aec1c24b34730616 |
ReconstructionCriterion | import torch
import torch.nn as nn
import torch.nn.functional as F
class ReconstructionCriterion(nn.Module):
"""
Here we calculate the criterion for -log p(x|z), we list two forms, the binary cross entropy form
as well as the mse loss form
"""
def __init__(self, x_sigma=1, bce_reconstruction=True... | 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... | PaperCodeSubmission/ICML2020-697 | ReconstructionCriterion | false | 8,656 | [
"MIT"
] | 12 | 00f7732c236b9c6234e76a47dfebe5de314d5c01 | https://github.com/PaperCodeSubmission/ICML2020-697/tree/00f7732c236b9c6234e76a47dfebe5de314d5c01 |
BertSelfAttention | # 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.... | HS-YN/PanoAVQA | BertSelfAttention | false | 18,376 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
FCDiscriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | JDAI-CV/FADA | FCDiscriminator | false | 13,871 | [
"Apache-2.0"
] | 120 | a1c6403963184a3427eda68cc94b03ff6143368a | https://github.com/JDAI-CV/FADA/tree/a1c6403963184a3427eda68cc94b03ff6143368a |
MixLoss | # 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... | lyakaap/pytorch-template | MixLoss | false | 15,978 | [
"MIT"
] | 140 | eff9f0a4dd50fa49c3b949065247598d5eabc91e | https://github.com/lyakaap/pytorch-template/tree/eff9f0a4dd50fa49c3b949065247598d5eabc91e |
Visual_Q_Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | wanghaodi/DQN_with_DDQN | Visual_Q_Network | false | 13,087 | [
"MIT"
] | 0 | 970ebf429c863debfd009b48e3bc4169fcbb05d4 | https://github.com/wanghaodi/DQN_with_DDQN/tree/970ebf429c863debfd009b48e3bc4169fcbb05d4 |
GLU | # 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.utils.data
assert_size_stride = torch._C._dyn... | Hcnaeg/DI-engine | GLU | false | 2,380 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
BinaryDiceLoss | # 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... | chenkarl/kits19 | BinaryDiceLoss | false | 12,209 | [
"MIT"
] | 0 | 7fa912320a23c6bf649566a1509aa493656b24c1 | https://github.com/chenkarl/kits19/tree/7fa912320a23c6bf649566a1509aa493656b24c1 |
Affine | # 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... | Uzair-Khattak/deit | Affine | false | 9,640 | [
"Apache-2.0"
] | 0 | 896004fc84d4ad2c4c9aa792822df7426af5903d | https://github.com/Uzair-Khattak/deit/tree/896004fc84d4ad2c4c9aa792822df7426af5903d |
UsBlockRes | # 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_... | MATHplus-Young-Academy/P2-Cardiac-Motion | UsBlockRes | false | 5,564 | [
"Apache-2.0"
] | 1 | 844995e8e5760f981c425d13c0bd7f2f3bb8baec | https://github.com/MATHplus-Young-Academy/P2-Cardiac-Motion/tree/844995e8e5760f981c425d13c0bd7f2f3bb8baec |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Angelinaa/KOBE | PositionwiseFeedForward | false | 51 | [
"MIT"
] | 0 | 4d25487051e2791a977e59297f70a25e51806466 | https://github.com/Angelinaa/KOBE/tree/4d25487051e2791a977e59297f70a25e51806466 |
LearnedUpsampling1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | austincap/samplernn-pytorch | LearnedUpsampling1d | false | 3,140 | [
"MIT"
] | 0 | d78399b899dcc116fd20823ae9e006ad8a6df4ea | https://github.com/austincap/samplernn-pytorch/tree/d78399b899dcc116fd20823ae9e006ad8a6df4ea |
Classifier | import torch
import torch.nn as nn
class Classifier(nn.Module):
"""MLP classifier
Parameters
----------
n_dimensions : int
Embedding dimension
n_classes : int
Number of classes.
"""
def __init__(self, n_dimensions, n_classes):
super().__init__()
self.n_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OrangeBaoWang/pyannote-audio | Classifier | false | 5,705 | [
"MIT"
] | 1 | ddbdf808f81e100ae8f463144fb7b3c32d8eba58 | https://github.com/OrangeBaoWang/pyannote-audio/tree/ddbdf808f81e100ae8f463144fb7b3c32d8eba58 |
ASP | # 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.... | albertvillanova/s3prl | ASP | false | 6,155 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
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... | Nayef211/tutorials | Symmetric | false | 9,566 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
SimpleAndModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAndModule(torch.nn.Module):
def __init__(self):
super(SimpleAndModule, self).__init__()
def forward(self, a, b):
c = torch.logical_and(a, b)
return torch.logical_and(c, c)
def get_inputs():
return [torc... | 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... | briancoutinho/glow | SimpleAndModule | false | 12,546 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
ResidualConnection | # 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... | maxkvant/LinearizedNNs | ResidualConnection | false | 7,177 | [
"Apache-2.0"
] | 1 | eb0198be70ca55e7463b97a5023d2f6ffe0f8ba6 | https://github.com/maxkvant/LinearizedNNs/tree/eb0198be70ca55e7463b97a5023d2f6ffe0f8ba6 |
MedianPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.optim
import torch.nn as nn
from torch.nn.modules.utils impo... | guzor/rgdb-semantic-segmentation | MedianPool2d | false | 12,475 | [
"MIT"
] | 0 | d9f3d8f1b2cb7357f64914bb873513dd16fad6df | https://github.com/guzor/rgdb-semantic-segmentation/tree/d9f3d8f1b2cb7357f64914bb873513dd16fad6df |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | DiceLoss | false | 13,318 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
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 ... | adriansarstedt/opacus | DPLSTMCell | false | 12,063 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 |
SCse | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | elmajdma/seismic-deeplearning | SCse | false | 15,310 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc |
CRFLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
class CRFLayer(nn.Module):
"""
"""
def __init__(self, output_dim):
super(CRFLayer, self).__init__()
self.output_dim = output_dim
self.trans = nn.Parameter(torch.Tensor(output_dim, outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Elvisambition/bert_seq2seq | CRFLayer | false | 5,178 | [
"Apache-2.0"
] | 1 | 643ac537c16872f0d13200de06001d8201a54fbb | https://github.com/Elvisambition/bert_seq2seq/tree/643ac537c16872f0d13200de06001d8201a54fbb |
ConvNet | import torch
import torch.optim
import torch.nn as nn
import torch.nn.functional as F
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.optim
import tor... | stanbiryukov/PyTorch-LBFGS | ConvNet | false | 16,496 | [
"MIT"
] | 451 | ea0ca553797b38d47682ce8ff553a4f53ec8c15c | https://github.com/stanbiryukov/PyTorch-LBFGS/tree/ea0ca553797b38d47682ce8ff553a4f53ec8c15c |
IA_gate | import torch
import torch.nn as nn
class IA_gate(nn.Module):
def __init__(self, in_dim, out_dim):
super(IA_gate, self).__init__()
self.IA = nn.Linear(in_dim, out_dim)
def forward(self, x, IA_head):
a = self.IA(IA_head)
a = 1.0 + torch.tanh(a)
a = a.unsqueeze(-1).unsqu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | huanglf714/COMatchNet | IA_gate | false | 6,821 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
HyperpriorSynthesis | import torch
import torch.nn as nn
import torch.nn.functional as F
class HyperpriorSynthesis(nn.Module):
"""
Hyperprior 'synthesis model' as proposed in [1]. Outputs
distribution parameters of input latents.
[1] Ballé et. al., "Variational image compression with a scale hyperprior",
arXiv: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
import torch.nn as nn
import ... | ahmedfgad/high-fidelity-generative-compression | HyperpriorSynthesis | false | 6,148 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
VisErrorLossV2 | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLossV2(nn.Module):
def __init__(self):
super(VisErrorLossV2, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint numbe... | 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.functi... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLossV2 | false | 15,421 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
DuelingNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DuelingNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(DuelingNetwork, self).__init__()
torch.manual_seed(seed)
hidden1 = 64
hidden2 = 64
self.fc1 = nn.Linear(state_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aishikawa/drl-impl | DuelingNetwork | false | 9,712 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
ResidualBlock | # 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... | fqhank/HESIC | ResidualBlock | false | 6,696 | [
"Apache-2.0"
] | 1 | f15cb8e6822af45f0022ea4887fce915e250ed75 | https://github.com/fqhank/HESIC/tree/f15cb8e6822af45f0022ea4887fce915e250ed75 |
SimpleSinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSinModule(torch.nn.Module):
def __init__(self):
super(SimpleSinModule, self).__init__()
def forward(self, a):
return torch.sin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | briancoutinho/glow | SimpleSinModule | false | 12,589 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
LeakyReLU | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def keep_variance_fn(x):
return x + 0.001
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))... | 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
import torch.nn as nn
from numbers import N... | collector-m/LiDAR-MOS | LeakyReLU | false | 15,060 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
TorchMul | import torch
class TorchMul(torch.nn.Module):
def __init__(self):
super(TorchMul, self).__init__()
def forward(self, x, y):
return torch.mul(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Akababa/torch2trt | TorchMul | false | 18,440 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
BiAttention | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch.nn.modules.module
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChCh1999/RTPB | BiAttention | false | 17,440 | [
"MIT"
] | 8 | 1066a3bfe4fe1b41eff74fd152936880302a60a2 | https://github.com/ChCh1999/RTPB/tree/1066a3bfe4fe1b41eff74fd152936880302a60a2 |
IdentityMessage | # 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | IdentityMessage | false | 508 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
InnerProductLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BELIEVEfxy/LightSANs | InnerProductLayer | false | 7,765 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
AdaptiveInstanceNorm | # 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 ... | mmhnoaccount/DeepChroma_128 | AdaptiveInstanceNorm | false | 7,262 | [
"MIT"
] | 1 | 337ec961bfc4ee44f48cb84e624c293ee2805b62 | https://github.com/mmhnoaccount/DeepChroma_128/tree/337ec961bfc4ee44f48cb84e624c293ee2805b62 |
GHMC | import torch
import torch.nn.functional as F
import torch.nn as nn
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChengBo5/mask-text-detector | GHMC | false | 258 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
FocalLoss | import torch
from torch import nn
from torchvision.datasets.folder import *
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss()
def forward(self, input, t... | 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... | tks1998/Pytorch-Face-recongition-state-of-the-art-Qmul-surveface- | FocalLoss | false | 4,488 | [
"MIT"
] | 0 | e4068db0c53a4c6b8e81127191687662806af8d8 | https://github.com/tks1998/Pytorch-Face-recongition-state-of-the-art-Qmul-surveface-/tree/e4068db0c53a4c6b8e81127191687662806af8d8 |
GroupedMultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features=in_features, out_features=
out_features, bias=bias)
self.noise = None
self.vn_std = No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | debasish-mihup/EfficientConformer | GroupedMultiHeadAttention | false | 10,343 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
MultiheadAttention | import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | VIRC-lab-csust/AGMNet | MultiheadAttention | false | 1,173 | [
"Apache-2.0"
] | 0 | ead95466da343cf9436774138c642d2ca12da4e4 | https://github.com/VIRC-lab-csust/AGMNet/tree/ead95466da343cf9436774138c642d2ca12da4e4 |
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Beximus/ResearchPortfolioCode | LayerNorm | false | 16,985 | [
"MIT"
] | 6 | db8343be6bbac361c3f6d01bbb82e458ff40f44e | https://github.com/Beximus/ResearchPortfolioCode/tree/db8343be6bbac361c3f6d01bbb82e458ff40f44e |
CatRepr | # 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... | csJd/CRANN | CatRepr | false | 9,939 | [
"MIT"
] | 0 | 8139b19b84ec11eff3c801185e4bfa974766d599 | https://github.com/csJd/CRANN/tree/8139b19b84ec11eff3c801185e4bfa974766d599 |
TransformerDecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
activation='relu'):
super(TransformerDecoderLayer, self).__init__()
self.multihead_attn = nn.MultiheadAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HumaticsLAB/GTM-Transformer | TransformerDecoderLayer | false | 17,414 | [
"MIT"
] | 7 | 94124d3246c7c22d8b952beeda53639a9ad170e3 | https://github.com/HumaticsLAB/GTM-Transformer/tree/94124d3246c7c22d8b952beeda53639a9ad170e3 |
MultVae | # 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.... | EricHe98/sad_final_project | MultVae | false | 17,269 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 |
KL_Loss | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils
class KL_Loss(nn.Module):
def __init__(self, temperature=1):
super(KL_Loss, self).__init__()
self.T = temperature
def forward(self, output_batch, teacher_outputs):
output_batch = F.log_softmax(output... | 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 ... | BlakeDai/FedML-test | KL_Loss | false | 9,192 | [
"Apache-2.0"
] | 0 | 3cb9a7234f3f0294f3137e4be572153ba7b62f8f | https://github.com/BlakeDai/FedML-test/tree/3cb9a7234f3f0294f3137e4be572153ba7b62f8f |
h_swish | import torch
from torch.nn import functional as F
import torch.nn as nn
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, self.inplace) / 6.0
return out * x
def get... | 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... | SpikeKing/MobileNetV3-Classification-PyTorch | h_swish | false | 11,890 | [
"MIT"
] | 0 | ab8d64c27ace7c70bfd1611bd8452947218d9b21 | https://github.com/SpikeKing/MobileNetV3-Classification-PyTorch/tree/ab8d64c27ace7c70bfd1611bd8452947218d9b21 |
Convolution | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn
a... | R1704/SpeechRecognitionSNN | Convolution | false | 967 | [
"MIT"
] | 0 | 4b788d1bd20d8ce201da6da8b200b3ca722c7efa | https://github.com/R1704/SpeechRecognitionSNN/tree/4b788d1bd20d8ce201da6da8b200b3ca722c7efa |
PlanarFlow | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class PlanarFlow(nn.Module):
"""Planar normalizing flow [Rezende & Mohamed 2015].
Provides a tighter bound on the ELBO by giving more expressive
power to the approximate distribution, such as by introducing
cova... | 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.utils.data
import torch.nn as nn
assert_size_stri... | gpoesia/variational-item-response-theory-public | PlanarFlow | false | 12,476 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
DPSLTMAdapter | # 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... | ffuuugor/opacus | DPSLTMAdapter | false | 6,726 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
HardMish | import torch
from torch import nn as nn
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 *... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation | HardMish | false | 8,295 | [
"Apache-2.0"
] | 34 | 2a546ef946989fc5bac8d819b3c93a9fdc83f241 | https://github.com/JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation/tree/2a546ef946989fc5bac8d819b3c93a9fdc83f241 |
ZeroLayer | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ZeroLayer(nn.Module):
def __init__(self, stride):
super(ZeroLayer, self).__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stri... | 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.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | ZeroLayer | false | 4,276 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
RobertaClassificationHead | import torch
import torch.nn as nn
from typing import Optional
class RobertaClassificationHead(nn.Module):
def __init__(self, num_classes, input_dim, inner_dim: 'Optional[int]'=
None, dropout: 'float'=0.1, activation=nn.ReLU):
super().__init__()
if not inner_dim:
inner_dim = i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ty... | ZongHR/text | RobertaClassificationHead | false | 3,003 | [
"BSD-3-Clause"
] | 0 | 856607154be7c784505869f10ae578346868b121 | https://github.com/ZongHR/text/tree/856607154be7c784505869f10ae578346868b121 |
MultiHead | # 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.... | xurantju/densecap | MultiHead | false | 11,050 | [
"BSD-3-Clause"
] | 0 | 2e58501e453bf98b9cc892e5b64997f5c1dfc808 | https://github.com/xurantju/densecap/tree/2e58501e453bf98b9cc892e5b64997f5c1dfc808 |
GaussianSubnetBlock | import torch
from torch import nn
class GaussianSubnetBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel, tanh=False):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel, padding=1 if
kernel > 1 else 0)
self.activation = nn.Tanh() if... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | laitalaj/cvpce | GaussianSubnetBlock | false | 7,064 | [
"MIT"
] | 1 | 7509e7d7783039f39a88edc6e411333bcf6fb2af | https://github.com/laitalaj/cvpce/tree/7509e7d7783039f39a88edc6e411333bcf6fb2af |
Ecgclient | # 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_... | JayDigvijay/Federated-Learning-and-Split-Learning-with-raspberry-pi | Ecgclient | false | 13,875 | [
"MIT"
] | 48 | 314a9618fc6be2ba1b9b7bdf93b126d49a2519ee | https://github.com/JayDigvijay/Federated-Learning-and-Split-Learning-with-raspberry-pi/tree/314a9618fc6be2ba1b9b7bdf93b126d49a2519ee |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | cheng-xie/dpgfddagger | Critic | false | 3,283 | [
"MIT"
] | 0 | 5264d5b9e0ab76fc9620da63bcfd78b25dadcbec | https://github.com/cheng-xie/dpgfddagger/tree/5264d5b9e0ab76fc9620da63bcfd78b25dadcbec |
C3 | import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
kernel_size=(5, 5))), ('relu3', nn.ReLU())]))
def forward(self, img):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | zjgbz/img_cls | C3 | false | 4,680 | [
"MIT"
] | 0 | 513d5ae423d95e008a82a6ffe443db49f8ed9ac2 | https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2 |
PatchEmbed | # 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... | WangFeng18/deit | PatchEmbed | false | 11,952 | [
"Apache-2.0"
] | 0 | 62a2c54faf683af8316fbec2e99f666879949cb4 | https://github.com/WangFeng18/deit/tree/62a2c54faf683af8316fbec2e99f666879949cb4 |
ChannelReplicate | # 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... | YingqiLiulll/scrips_for_SR | ChannelReplicate | false | 1,250 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | AppleHolic/fairseq | ZeroPad1d | false | 13,321 | [
"MIT"
] | 429 | c5b32cb2bde59a7bb7987b22864731fe927523d4 | https://github.com/AppleHolic/fairseq/tree/c5b32cb2bde59a7bb7987b22864731fe927523d4 |
SingleLayer | import torch
import torch.nn as nn
class SingleLayer(nn.Module):
def __init__(self, nChannels, growthRate):
super(SingleLayer, self).__init__()
self.bn1 = nn.GroupNorm(nChannels, nChannels, affine=True)
self.conv1 = nn.Conv2d(nChannels, growthRate, kernel_size=3,
padding=1, bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cadurosar/graph_kd_dense_cifar100 | SingleLayer | false | 1,633 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
CustomBatchNormManualModule | # 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_... | askliar/deep_learning | CustomBatchNormManualModule | false | 1,489 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
ConvLayer | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | GEN418/EventGAN | ConvLayer | false | 477 | [
"MIT"
] | 0 | 372318bc8f285f513db4babf7786b5c04e97c86d | https://github.com/GEN418/EventGAN/tree/372318bc8f285f513db4babf7786b5c04e97c86d |
MultiHeadAttentionLayer | # 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.... | mmwebster/DeepRL-Grounding | MultiHeadAttentionLayer | false | 12,800 | [
"MIT"
] | 0 | aa7fa63fbc26e8b0fa3fe289a5fe5a00ef3e6278 | https://github.com/mmwebster/DeepRL-Grounding/tree/aa7fa63fbc26e8b0fa3fe289a5fe5a00ef3e6278 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, encoder_dim, hidden_dim):
super(Attention, self).__init__()
self.hidden_lin = nn.Linear(hidden_dim, hidden_dim)
self.tanh = nn.Tanh()
self.img_lin = nn.Linear(encoder_dim, hidden_dim)
self.so... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Soumya1612-Rasha/Image-Captioning | Attention | false | 2,854 | [
"MIT"
] | 0 | 63439754567ced2dbe762aed150ba5476029781c | https://github.com/Soumya1612-Rasha/Image-Captioning/tree/63439754567ced2dbe762aed150ba5476029781c |
BasicAttentionLayer | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | BasicAttentionLayer | false | 5,487 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 |
BatchDense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | iloncka/neurotrees | BatchDense | false | 10,233 | [
"MIT"
] | 0 | ddb52dc0e7ac1cf67a426b401ba06149807e03ec | https://github.com/iloncka/neurotrees/tree/ddb52dc0e7ac1cf67a426b401ba06149807e03ec |
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... | mhmorta/continual-learning-1 | LinearExcitability | false | 4,004 | [
"MIT"
] | 0 | 959d5238d4dd015245592993b5d044572ab58c90 | https://github.com/mhmorta/continual-learning-1/tree/959d5238d4dd015245592993b5d044572ab58c90 |
TwoMLPHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GreenCUBIC/Gas-Prices-of-America | TwoMLPHead | false | 2,335 | [
"MIT"
] | 0 | e2a045db99d061b5d2acbe208da8cc19af12659d | https://github.com/GreenCUBIC/Gas-Prices-of-America/tree/e2a045db99d061b5d2acbe208da8cc19af12659d |
CompositeActivation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ndey96/lucent | CompositeActivation | false | 10,602 | [
"Apache-2.0"
] | 0 | d868d8ca52520bd245c1e5fcf3b026782f77e561 | https://github.com/ndey96/lucent/tree/d868d8ca52520bd245c1e5fcf3b026782f77e561 |
Critic | import torch
import torch.nn as nn
import torch as t
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, actio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LeonLester/Machin-title-in-progress- | Critic | false | 11,648 | [
"MIT"
] | 0 | 777479d47b520dcdc6b09c247591b5fe1d6cbe8c | https://github.com/LeonLester/Machin-title-in-progress-/tree/777479d47b520dcdc6b09c247591b5fe1d6cbe8c |
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