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 |
|---|---|---|---|---|---|---|---|---|---|---|
h_swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dx9527/MobileNetV3-pytorch | h_swish | false | 15,279 | [
"MIT"
] | 291 | 7812dbcedd5db4e3bbfc21122b82205848f742cf | https://github.com/dx9527/MobileNetV3-pytorch/tree/7812dbcedd5db4e3bbfc21122b82205848f742cf |
GluMlp | import torch
from torch import nn
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.Sigmoid, drop=0.0):
super().__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SimonCqk/towhee | GluMlp | false | 9,632 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
AvgPoolWithMask | # 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... | lonePatient/TorchBlocks | AvgPoolWithMask | false | 15,949 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
ItemInferenceNetwork | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | BratChar/variational-item-response-theory-public | ItemInferenceNetwork | false | 13,406 | [
"MIT"
] | 52 | 12862157e99506a0ed7018f1b8a485d4e61fb5bf | https://github.com/BratChar/variational-item-response-theory-public/tree/12862157e99506a0ed7018f1b8a485d4e61fb5bf |
Upsample | import torch
from torch import nn
import torch.utils.data
import torch
class Upsample(nn.Module):
def __init__(self, scale):
super(Upsample, self).__init__()
self.up = nn.Upsample(scale_factor=scale, mode='bicubic',
align_corners=True)
def forward(self, x):
return self.up... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import ... | LiFH/MySR | Upsample | false | 780 | [
"MIT"
] | 0 | f6075f8711853aba6f0aae9cef18c5da84abb78c | https://github.com/LiFH/MySR/tree/f6075f8711853aba6f0aae9cef18c5da84abb78c |
MapReduce | import torch
import torch.nn as nn
class MapReduce(nn.Module):
"""
Reduce feature maps into a single edge map
"""
def __init__(self, channels):
super(MapReduce, self).__init__()
self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
nn.init.constant_(self.conv.bias, 0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ZitongYu/pidinet | MapReduce | false | 18,202 | [
"MIT"
] | 5 | 15cdf9fb056549934877675bf7571b427f86db55 | https://github.com/ZitongYu/pidinet/tree/15cdf9fb056549934877675bf7571b427f86db55 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | IewNixIl/graduation_project_under | Net | false | 9,278 | [
"MIT"
] | 0 | 67d0345208511bb06c35c3453227b2fa4ebef4a3 | https://github.com/IewNixIl/graduation_project_under/tree/67d0345208511bb06c35c3453227b2fa4ebef4a3 |
MLP | import torch
from typing import Optional
from torch import nn
from collections import OrderedDict
class MLP(nn.Module):
def __init__(self, in_channel: 'int', out_channel: 'int',
hidden_channel: 'int', n_linear: 'int', activation:
'Optional[nn.Module]'=None):
super().__init__()
ass... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Optional
f... | HiroakiMikami/mlprogram | MLP | false | 17,370 | [
"MIT"
] | 9 | 573e94c567064705fa65267dd83946bf183197de | https://github.com/HiroakiMikami/mlprogram/tree/573e94c567064705fa65267dd83946bf183197de |
ModMBStddevLayer | # 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_... | Sardhendu/mmediting | ModMBStddevLayer | false | 9,891 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
TorchModule | # 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
ass... | Cher-B/ivy | TorchModule | false | 13,494 | [
"Apache-2.0"
] | 161 | 95273172201071ebf7b83d56bb314450ebe41071 | https://github.com/Cher-B/ivy/tree/95273172201071ebf7b83d56bb314450ebe41071 |
ExampleBackbone | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | CarnoZhao/mmsegmentation | ExampleBackbone | false | 7,847 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
Posterior | # 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.... | morimo27182/DeepKalmanFilter | Posterior | false | 12,799 | [
"MIT"
] | 0 | 5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 | https://github.com/morimo27182/DeepKalmanFilter/tree/5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 |
MyCustomFunctionReluModel | # 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
import torch.onnx
import torch.utils.checkpoint
assert_size_stride = torc... | almiliMSFT/onnxruntime | MyCustomFunctionReluModel | false | 14,801 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
SimpleATanModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleATanModule(torch.nn.Module):
def __init__(self):
super(SimpleATanModule, self).__init__()
def forward(self, a):
return torch.atan(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 libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleATanModule | false | 14,649 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MsgNorm | # 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
assert_size_stride = torch._... | Hermine2015/deep_gcns_torch | MsgNorm | false | 5,334 | [
"MIT"
] | 1 | 69524a2a5de2ba4c3adb0fea0a090b3e9b4510d4 | https://github.com/Hermine2015/deep_gcns_torch/tree/69524a2a5de2ba4c3adb0fea0a090b3e9b4510d4 |
SpatialGatherModule | # 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.... | AnonSubmission6150/submission6150 | SpatialGatherModule | false | 8,992 | [
"Apache-2.0"
] | 0 | 571633d9a12b4fd7a9546947787fc068966dab04 | https://github.com/AnonSubmission6150/submission6150/tree/571633d9a12b4fd7a9546947787fc068966dab04 |
RMSNorm | import torch
from torch import nn
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-08):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.sc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | ncoop57/x-transformers | RMSNorm | false | 10,607 | [
"MIT"
] | 0 | b65f25384349abfc101001b42482b05745c861fa | https://github.com/ncoop57/x-transformers/tree/b65f25384349abfc101001b42482b05745c861fa |
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.... | alipay/Pyraformer | EncoderLayer | false | 18,314 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
Encoder | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias, stride=stride)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
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
from torch._inductor.runtime.... | WdBlink/AugMix-3DOCUNet-Brats2019 | Encoder | false | 5,965 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
Generator | # 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.... | CMU-IDS-2020/fp-index-out-of-bounds | Generator | false | 196 | [
"BSD-3-Clause"
] | 0 | 1a9fdaac8288a980e759a0c2e46f28294d25c71f | https://github.com/CMU-IDS-2020/fp-index-out-of-bounds/tree/1a9fdaac8288a980e759a0c2e46f28294d25c71f |
ANN | import torch
import torch.nn as nn
import torch.nn.functional as F
class ANN(nn.Module):
def __init__(self, n_li, n_l1, n_l2, n_l3, n_lo):
super(ANN, self).__init__()
self.lin_in = nn.Linear(n_li, n_l1)
self.lin_h1 = nn.Linear(n_l1, n_l2)
self.lin_h2 = nn.Linear(n_l2, n_l3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | aixpact/python | ANN | false | 1,385 | [
"MIT"
] | 0 | 41256672472fec2c0f8bf6a9146c4053b16fd907 | https://github.com/aixpact/python/tree/41256672472fec2c0f8bf6a9146c4053b16fd907 |
TripletLoss | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import *
from torch.optim.lr_scheduler import *
def _batch_hard(mat_distance, mat_similarity, indice=False):
sorted_mat_distance, positive_indices = torch.sort(mat_distance + -
9999999.0 * (1 - mat_similarity), dim=1, descendi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dingyuan-Zheng/ctf-UDA | TripletLoss | false | 370 | [
"MIT"
] | 0 | 3e3c67f68d7eb0b52a16a259e5a77e153062c4fd | https://github.com/Dingyuan-Zheng/ctf-UDA/tree/3e3c67f68d7eb0b52a16a259e5a77e153062c4fd |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AlexanderDokuchaev/mmsegmentation | PPMConcat | false | 11,176 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
InitConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class InitConv(nn.Module):
def __init__(self, in_channels=4, out_channels=16, dropout=0.2):
super(InitConv, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
padding=1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | potpov/TransBTS | InitConv | false | 16,274 | [
"Apache-2.0"
] | 163 | 658de5f1dde17d25db54fb07adf49370cc32d7c3 | https://github.com/potpov/TransBTS/tree/658de5f1dde17d25db54fb07adf49370cc32d7c3 |
SelfAttentionSublayer | # 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.... | Nickeilf/pysimt | SelfAttentionSublayer | false | 9,524 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ajy8456/active-recognition | Actor | false | 3,052 | [
"MIT"
] | 0 | 7d3a4bbfceaf5fb32cd43f62636f36a10ab63807 | https://github.com/ajy8456/active-recognition/tree/7d3a4bbfceaf5fb32cd43f62636f36a10ab63807 |
Synthesis_prior_net | import math
import torch
import torch.nn as nn
import torch.utils.data
class Synthesis_prior_net(nn.Module):
"""
Decode synthesis prior
"""
def __init__(self, out_channel_N=192, out_channel_M=320):
super(Synthesis_prior_net, self).__init__()
self.deconv1 = nn.ConvTranspose2d(out_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | Synthesis_prior_net | false | 4,549 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
TransformerBlock | import math
import torch
import torch.nn.functional as F
def gelu(x):
"""
GELU activation function.
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class MultiHeadedAttention(torch.nn.Module):
"""
Implement of multi-head attention.
"""
def __init__(self, n_heads, hidden_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.triton_helpers import libdevice, math as tl_math
im... | haophancs/TREQS | TransformerBlock | false | 15,519 | [
"MIT"
] | 149 | 49e354ce2a08cf963ec139d99936020e0f80ced8 | https://github.com/haophancs/TREQS/tree/49e354ce2a08cf963ec139d99936020e0f80ced8 |
MSECompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | rivergold/mmediting | MSECompositionLoss | false | 7,573 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
Mutan | # 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... | AndresPMD/GCN_classification | Mutan | false | 7,931 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
PMA | # 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.... | ClintvanHoesel/MXMNet_adapted | PMA | false | 325 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
SimpleLogModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogModule(torch.nn.Module):
def __init__(self, *dimensions):
super(SimpleLogModule, self).__init__()
def forward(self, a):
b = torch.log(a)
return torch.log(b)
def get_inputs():
return [torch.rand([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 math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleLogModule | false | 14,666 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
BertOutput | # 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... | HS-YN/PanoAVQA | BertOutput | false | 18,391 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
C3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | ConstantinSeibold/SGL | C3 | false | 17,133 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(Sine, self).__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | CGruich/ocp | Sine | false | 11,251 | [
"MIT",
"BSD-3-Clause"
] | 0 | dd97972b39d4a05e37f745e393a5245657ef5f9e | https://github.com/CGruich/ocp/tree/dd97972b39d4a05e37f745e393a5245657ef5f9e |
DocumentEncoder | # 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... | huyi-work/UnifiedEmbeddingModel | DocumentEncoder | false | 15,563 | [
"MIT"
] | 50 | 85c8442122213d1f1b1027df0fd34f428259aaa4 | https://github.com/huyi-work/UnifiedEmbeddingModel/tree/85c8442122213d1f1b1027df0fd34f428259aaa4 |
iRMSE | # 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_... | anglixjtu/MSG_CHN_WACV20 | iRMSE | false | 14,859 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.b_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.... | amaankhan02/ChaLearn-2021-LAP | PositionwiseFeedForward | false | 18,285 | [
"Apache-2.0",
"MIT"
] | 5 | 73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 | https://github.com/amaankhan02/ChaLearn-2021-LAP/tree/73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 |
EltwiseProdScoring | # 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... | zhangybzbo/speaker_follower | EltwiseProdScoring | false | 16,803 | [
"BSD-2-Clause",
"MIT"
] | 117 | e4d109ee26b2f57066adc9720443abf842ee9a9d | https://github.com/zhangybzbo/speaker_follower/tree/e4d109ee26b2f57066adc9720443abf842ee9a9d |
CEFL | # 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
... | umairjavaid/staff-employee-classification | CEFL | false | 13,060 | [
"MIT"
] | 0 | fc5fe32acfbde2b188094df90d888eeb0f4f4acd | https://github.com/umairjavaid/staff-employee-classification/tree/fc5fe32acfbde2b188094df90d888eeb0f4f4acd |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | sgrimbly/lets-do-irl | Actor | false | 4,305 | [
"MIT"
] | 0 | 4233e238342394feef6a7bd495cc6b700d435b00 | https://github.com/sgrimbly/lets-do-irl/tree/4233e238342394feef6a7bd495cc6b700d435b00 |
FCDiscriminator | import torch
import torch.nn as nn
class FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YoNyeoSeok/AsymTri | FCDiscriminator | false | 12,025 | [
"MIT"
] | 0 | a5a9a4b92074d770ed57802ff26b149a301cf4a4 | https://github.com/YoNyeoSeok/AsymTri/tree/a5a9a4b92074d770ed57802ff26b149a301cf4a4 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, observations_dim, actions_dim, hidden_dim=500):
super(Net, self).__init__()
self._input_layer = nn.Linear(observations_dim, hidden_dim)
self._hidden1 = nn.Linear(hidden_dim, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | hany606/PMLDL-Project | Net | false | 3,568 | [
"MIT"
] | 0 | 40ccf97720c8fd28ed2a8d8101a0499ff58c2b38 | https://github.com/hany606/PMLDL-Project/tree/40ccf97720c8fd28ed2a8d8101a0499ff58c2b38 |
GDL | import torch
import numpy as np
from torch import nn
import torch.nn.functional
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=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
import numpy as np
from torch import nn
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | BRAIN-Lab-UNC/BrainExtraction-TissueSegmentation-Macaque | GDL | false | 13,370 | [
"MIT"
] | 770 | b5329035d9e32c8a27151cf2396eaf209396a334 | https://github.com/BRAIN-Lab-UNC/BrainExtraction-TissueSegmentation-Macaque/tree/b5329035d9e32c8a27151cf2396eaf209396a334 |
OutputBlock | # 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... | XaviGurrola/RDUNet | OutputBlock | false | 9,649 | [
"MIT"
] | 0 | 549fc88c6faef1b310773944fc3988e22030d94d | https://github.com/XaviGurrola/RDUNet/tree/549fc88c6faef1b310773944fc3988e22030d94d |
MLPDecoder | # 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 ... | bostdiek/DarkMachinesAutoEncoder | MLPDecoder | false | 3,245 | [
"MIT"
] | 0 | f05f482b1bbd79cd777221bfe0d37e75b72c3e2b | https://github.com/bostdiek/DarkMachinesAutoEncoder/tree/f05f482b1bbd79cd777221bfe0d37e75b72c3e2b |
Ecgclient | import torch
import torch.nn as nn
class Ecgclient(nn.Module):
def __init__(self):
super(Ecgclient, self).__init__()
self.conv1 = nn.Conv1d(1, 16, 7, padding=3)
self.relu1 = nn.LeakyReLU()
self.pool1 = nn.MaxPool1d(2)
self.conv2 = nn.Conv1d(16, 16, 5, padding=2)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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 |
FusedLeakyReLU | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slop... | 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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asse... | Theomat/colorization-av-enseirb-2020 | FusedLeakyReLU | false | 14,486 | [
"Apache-2.0"
] | 1,422 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | https://github.com/Theomat/colorization-av-enseirb-2020/tree/c54c2388ea39a62289fa2f1c51b4757bf55d3c4f |
Net_Tran | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Net_Tran(nn.Module):
def __init__(self):
super(Net_Tran, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=2, padding=1)
self.deconv1 = 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
import torch.nn.parallel
import torch.optim
import torch.u... | YibinXie/Pose_Estimation | Net_Tran | false | 2,975 | [
"MIT"
] | 0 | 5849140bf842bf3aeaad75827f5e7b7f2999c9ee | https://github.com/YibinXie/Pose_Estimation/tree/5849140bf842bf3aeaad75827f5e7b7f2999c9ee |
TemporalPooling | # 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.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | peter-yys-yoon/traditional-dance-recognition | TemporalPooling | false | 12,958 | [
"Apache-2.0"
] | 0 | be4939d53b838624a04dba0826532c65421d1325 | https://github.com/peter-yys-yoon/traditional-dance-recognition/tree/be4939d53b838624a04dba0826532c65421d1325 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, hidden_size, num_inputs, num_outputs):
super(Actor, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HAXRD/PIC | Actor | false | 8,182 | [
"MIT"
] | 28 | 658b4dd6b01e64413d5f8f0107d9167f1bd78546 | https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546 |
Debugnetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn import init
class conv(nn.Module):
"""
n*n conv with relu
"""
def __init__(self, in_dim, out_dim, kernal_size, stride, padding):
super(conv, self).__init__()
self.con_layer = nn.Conv2d(in_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 to... | H-Liu1997/Pytorch_Pose_Estimation_Framework | Debugnetwork | false | 7,266 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
MulScalarNegative | # 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
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyna... | Mookel/tvm | MulScalarNegative | false | 14,063 | [
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 90 | ae58f2c387de9944d241a083ce9a0dd4c9ae613d | https://github.com/Mookel/tvm/tree/ae58f2c387de9944d241a083ce9a0dd4c9ae613d |
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.... | Oktai15/NeMo | MultiHeadAttention | false | 5,699 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='mean'):
super().__init__()
self.bce = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
self.gamma = gamma
if reduct... | 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... | botkop/lark | FocalLoss | false | 1,571 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
InnerProductDecoder | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | leiyu-thunder/gae_dgl | InnerProductDecoder | false | 7,069 | [
"Apache-2.0"
] | 1 | c743acc96e24c4ca3ae72d08956381f302b373bd | https://github.com/leiyu-thunder/gae_dgl/tree/c743acc96e24c4ca3ae72d08956381f302b373bd |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | MartinHahner/OpenPCDet | SigmoidFocalClassificationLoss | false | 14,006 | [
"Apache-2.0"
] | 1,984 | 9375908d30ee5023355ebdd77041d7f2cbfd7ec8 | https://github.com/MartinHahner/OpenPCDet/tree/9375908d30ee5023355ebdd77041d7f2cbfd7ec8 |
GeM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | AlessandroRigoli/project_vg | GeM | false | 11,156 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 |
LabelSmoothing | # 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
... | mrblasco/kaggle_moa_winner_hungry_for_gold | LabelSmoothing | false | 16,118 | [
"Apache-2.0"
] | 89 | 00df6d0aa4a48e526cee3e36f6e723a1534bfa08 | https://github.com/mrblasco/kaggle_moa_winner_hungry_for_gold/tree/00df6d0aa4a48e526cee3e36f6e723a1534bfa08 |
ConvEncoder | import torch
import torch.nn as nn
from torch.nn import functional as F
class ConvEncoder(nn.Module):
def __init__(self, embedding_size, act_fn='relu'):
super().__init__()
self.act_fn = getattr(F, act_fn)
self.embedding_size = embedding_size
self.conv_1 = nn.Conv2d(3, 32, 4, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 to... | alec-tschantz/planet | ConvEncoder | false | 18,284 | [
"MIT"
] | 7 | bf68722993c93129263bb9606a582d24cb4f2a58 | https://github.com/alec-tschantz/planet/tree/bf68722993c93129263bb9606a582d24cb4f2a58 |
NegSamplingLoss | import torch
import torch.nn as nn
class NegSamplingLoss(nn.Module):
def __init__(self):
super(NegSamplingLoss, self).__init__()
def forward(self, score, sign):
return -torch.mean(torch.sigmoid(sign * score))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | MIracleyin/RecBole-notebook | NegSamplingLoss | false | 9,560 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
FCTestNN | # 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_... | NirooshKa/APS360-Cold-Start-Problem | FCTestNN | false | 9,449 | [
"MIT"
] | 0 | 4c864737b4e6db992e99610a0ed8e82c957fd6cc | https://github.com/NirooshKa/APS360-Cold-Start-Problem/tree/4c864737b4e6db992e99610a0ed8e82c957fd6cc |
localSubNet | import torch
import torch.nn as nn
class localSubNet(nn.Module):
def __init__(self, blockDepth=16, convDepth=32, scale=0.25):
super(localSubNet, self).__init__()
self.blockDepth = blockDepth
self.convDepth = convDepth
self.scale = scale
self.net = torch.nn.Sequential()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | shir-barzel-healthy/CIE_XYZ_NET | localSubNet | false | 16,453 | [
"MIT"
] | 64 | 9aabf5222dd81efa518233340dc3313177927e27 | https://github.com/shir-barzel-healthy/CIE_XYZ_NET/tree/9aabf5222dd81efa518233340dc3313177927e27 |
Grouping | # 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
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | HughMun/MultiBench | Grouping | false | 13,796 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
Generator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.l1 = torch.nn.Linear(z_dim, 256)
self.l2 = torch.nn.Linear(256, 256)
self.l3 = torch.nn.Linear(256, 256)
self.l4 = torch.nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Phutoast/Win-or-Learn-Fast | Generator | false | 17,809 | [
"MIT"
] | 7 | 5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 | https://github.com/Phutoast/Win-or-Learn-Fast/tree/5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 |
Mul | # 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... | Akababa/torch2trt | Mul | false | 18,409 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
GCNModelVAE | # 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... | peterfeifanchen/scGNN | GCNModelVAE | false | 16,265 | [
"MIT"
] | 60 | 4ef9013ad0f44f9f51708e9bb60e5138f5706593 | https://github.com/peterfeifanchen/scGNN/tree/4ef9013ad0f44f9f51708e9bb60e5138f5706593 |
PairwiseRankingLoss | import torch
import torch.nn as nn
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ktodorov/uva-semantics-19 | PairwiseRankingLoss | false | 3,850 | [
"MIT"
] | 0 | c20e4f1d00f6693a8a46dd1d5576cfd3adced896 | https://github.com/ktodorov/uva-semantics-19/tree/c20e4f1d00f6693a8a46dd1d5576cfd3adced896 |
Log1Exp | import torch
from torch import nn
def log1exp(x):
return torch.log(1.0 + torch.exp(x))
class Log1Exp(nn.Module):
def forward(self, x):
return log1exp(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 math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | dattientran/attorch | Log1Exp | false | 12,387 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
AE_big | # 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 | false | 12,446 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilin... | 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... | ChenChengKuan/DGI | Discriminator | false | 11,296 | [
"MIT"
] | 0 | 432bf78418b8dd52648c9cac45e8841bee4c5032 | https://github.com/ChenChengKuan/DGI/tree/432bf78418b8dd52648c9cac45e8841bee4c5032 |
SequenceBias | # 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.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Pa... | ffuuugor/opacus | SequenceBias | false | 6,690 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
Entmax15 | from torch.autograd import Function
import torch
import torch.nn as nn
def _make_ix_like(X, dim):
d = X.size(dim)
rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype)
view = [1] * X.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def _roll_last(X, dim):
if dim == -1:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import F... | mtreviso/entmax | Entmax15 | false | 10,650 | [
"MIT"
] | 0 | 5b029d07fe00d7aacc77c8e684a5796d29287575 | https://github.com/mtreviso/entmax/tree/5b029d07fe00d7aacc77c8e684a5796d29287575 |
RewardCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | chagmgang/object_relation_transformer | RewardCriterion | false | 6,412 | [
"MIT"
] | 1 | 04b88514f97232c12b576720e4b82226751c3c48 | https://github.com/chagmgang/object_relation_transformer/tree/04b88514f97232c12b576720e4b82226751c3c48 |
ProtoLoss | import torch
class ProtoLoss(torch.nn.Module):
def __init__(self, num_classes, num_support, num_queries, ndim):
super(ProtoLoss, self).__init__()
self.num_classes = num_classes
self.num_support = num_support
self.num_queries = num_queries
self.ndim = ndim
def euclidea... | 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
assert_size_stride = t... | gradjitta/Prototypical-Networks | ProtoLoss | false | 10,191 | [
"MIT"
] | 0 | 9ec344f7299353889e2087224b80a74519ca1a3c | https://github.com/gradjitta/Prototypical-Networks/tree/9ec344f7299353889e2087224b80a74519ca1a3c |
ChannelAttentionModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rushirajsherlocked/External-Attention-pytorch | ChannelAttentionModule | false | 4,233 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
FactoredAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch as t
def checkpoint(func, inputs, params, flag):
if flag:
args = inputs + tuple(par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jovonni/jukebox | FactoredAttention | false | 735 | [
"MIT"
] | 0 | 965a6f78aae67506a6e4fcdb205e2c39132e12e0 | https://github.com/Jovonni/jukebox/tree/965a6f78aae67506a6e4fcdb205e2c39132e12e0 |
cnn_layer | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class cnn_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(cnn_layer, self).__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | db-bionlp/CLNER | cnn_layer | false | 15,160 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
MultiHeadAttn | # 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.... | CallMeSp/My_flowQA | MultiHeadAttn | false | 214 | [
"Apache-2.0"
] | 0 | 87d82551f614b089771b22a1992e2be41a2995b3 | https://github.com/CallMeSp/My_flowQA/tree/87d82551f614b089771b22a1992e2be41a2995b3 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision import datas... | adam-dziedzic/ASL | SEModule | false | 12,113 | [
"MIT"
] | 0 | cc063f5e7eda1498544ad2c3b224985203b0774a | https://github.com/adam-dziedzic/ASL/tree/cc063f5e7eda1498544ad2c3b224985203b0774a |
GGCL_F | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class GGCL_F(Module):
"""Graph Gaussian Convolution Layer (GGCL) when the input is feature"""
def __init__(self, in_features, out_features, dropout=0.6)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | marblet/DeepRobust | GGCL_F | false | 10,559 | [
"MIT"
] | 0 | 126c05818e38062c2423cd40dc8937ccc43c738b | https://github.com/marblet/DeepRobust/tree/126c05818e38062c2423cd40dc8937ccc43c738b |
upsample | import torch
import torch.nn as nn
class upsample(nn.Module):
def __init__(self):
super(upsample, self).__init__()
self.upsample = torch.nn.UpsamplingBilinear2d([256, 256])
def forward(self, input):
return (self.upsample(input) + 1.0) / 2
def get_inputs():
return [torch.rand([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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Euiyeon-Kim/SuperFAN-Pytorch | upsample | false | 5,140 | [
"MIT"
] | 1 | 4a18e559c4b91d0d422b66e63509aeea8a7dc8f2 | https://github.com/Euiyeon-Kim/SuperFAN-Pytorch/tree/4a18e559c4b91d0d422b66e63509aeea8a7dc8f2 |
DepthWiseSeparableConv2d | import torch
import torch.nn as nn
import torch.jit
import torch.nn
class DepthWiseSeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=1, dilation=1, bias=True):
"""Depthwise separable 2D convolution.
Args:
in_channels (int): number ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.jit
import torch.nn
assert_size_stride = torc... | ankmathur96/torchsupport | DepthWiseSeparableConv2d | false | 3,198 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
TopkMSELoss | import torch
class TopkMSELoss(torch.nn.Module):
def __init__(self, topk) ->None:
super().__init__()
self.topk = topk
self.criterion = torch.nn.MSELoss(reduction='none')
def forward(self, output, label):
losses = self.criterion(output, label).mean(2).mean(1)
losses = ... | 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... | alipay/Pyraformer | TopkMSELoss | false | 18,280 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HenryOsborne/LearningToPaint | Discriminator | false | 9,224 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb |
generator | # 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_... | bblinn2017/IM-NET-pytorch | generator | false | 3,194 | [
"MIT"
] | 0 | 82ff646aaf2f93ae1560debb40fe05f1420ff655 | https://github.com/bblinn2017/IM-NET-pytorch/tree/82ff646aaf2f93ae1560debb40fe05f1420ff655 |
DenseBlock | # 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... | gaetangate/FewRel | DenseBlock | false | 12,410 | [
"MIT"
] | 0 | 150199d1060571315b1f370b3b3352d7a7c72dd5 | https://github.com/gaetangate/FewRel/tree/150199d1060571315b1f370b3b3352d7a7c72dd5 |
AE_2D_v4 | # 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_2D_v4 | false | 12,442 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
LSTMCell | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class LSTMCell(Module):
"""
## Long Short-Term Memory Cell
LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
and $h$ is like the short term memory.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | techthiyanes/annotated_deep_learning_paper_implementations | LSTMCell | false | 16,560 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
TenLayerNet | # 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
assert_size_stride = torch._C... | anvitha-bhat/iot_final_project | TenLayerNet | false | 9,750 | [
"MIT"
] | 0 | e9301c083d5e7a228d0ad868e44cb1df3a5f7363 | https://github.com/anvitha-bhat/iot_final_project/tree/e9301c083d5e7a228d0ad868e44cb1df3a5f7363 |
skip_connection | # 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... | CMI-Laboratory/CAE | skip_connection | false | 11,272 | [
"Apache-2.0"
] | 0 | 11c94f2152a51c9d4e86f8956ea75c575094256b | https://github.com/CMI-Laboratory/CAE/tree/11c94f2152a51c9d4e86f8956ea75c575094256b |
AdaptiveInstanceNorm | import torch
import torch.utils.data
import torch
import torch.nn as nn
class AdaptiveInstanceNorm(nn.Module):
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.style = nn.Linear(style_dim, in_channel * 2)
self.style.weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | yuhongherald/pytorch-CycleGAN-and-pix2pix | AdaptiveInstanceNorm | false | 4,640 | [
"BSD-3-Clause"
] | 0 | 48cb3aa46fde39684db9c24586fcec6781138e2a | https://github.com/yuhongherald/pytorch-CycleGAN-and-pix2pix/tree/48cb3aa46fde39684db9c24586fcec6781138e2a |
LReluCustom | # 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... | kkulczak/phrases_reconstruction_GAN | LReluCustom | false | 3,835 | [
"MIT"
] | 0 | 5cf273416eb714f813a8d603942a442f0933cbff | https://github.com/kkulczak/phrases_reconstruction_GAN/tree/5cf273416eb714f813a8d603942a442f0933cbff |
RobertaClassificationHead_R | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class RobertaClassificationHead_R(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_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.triton_helpers import libdevice
from torch import n... | Delecis/bert-classification | RobertaClassificationHead_R | false | 10,019 | [
"Apache-2.0"
] | 0 | 00e0d295ecf22a1bd364f2d63244469692ff23a3 | https://github.com/Delecis/bert-classification/tree/00e0d295ecf22a1bd364f2d63244469692ff23a3 |
SFU | # 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 ... | MobtgZhang/MWMLNet | SFU | false | 5,608 | [
"MIT"
] | 1 | 125bb39935916b6b4be505c51cb6a04eb49b96d0 | https://github.com/MobtgZhang/MWMLNet/tree/125bb39935916b6b4be505c51cb6a04eb49b96d0 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Danish-VSL/deep-person-reid | SEModule | false | 13,566 | [
"MIT"
] | 244 | 2e3a4b6706b84c77203f9905683b917ab0871b93 | https://github.com/Danish-VSL/deep-person-reid/tree/2e3a4b6706b84c77203f9905683b917ab0871b93 |
TemporallyBatchedAdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(nn.Module):
def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim,
internal_dim=None):
super(AdditiveAttention, self).__init__()
if internal_dim is None:
internal_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
from torch._inductor.runtime.... | Vision-CAIR/UnlikelihoodMotionForecasting | TemporallyBatchedAdditiveAttention | false | 5,944 | [
"MIT"
] | 1 | 556d6a3ed3e4e0e2d88108d7dbb48933313b58aa | https://github.com/Vision-CAIR/UnlikelihoodMotionForecasting/tree/556d6a3ed3e4e0e2d88108d7dbb48933313b58aa |
Sine | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | justinjohn0306/CIPS-3D | Sine | false | 7,005 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
OrModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ArjitJ/tbd-nets | OrModule | false | 8,852 | [
"MIT"
] | 0 | 8e93ecad54489706ec3249c9ca5d345d6866e1ba | https://github.com/ArjitJ/tbd-nets/tree/8e93ecad54489706ec3249c9ca5d345d6866e1ba |
PairwiseDistance | import torch
class PairwiseDistance(torch.nn.Module):
def __init__(self, p=2):
super().__init__()
self.p = p
def forward(self, x, y):
x_ = x.repeat([1] + list(y.shape[1:])).reshape(*y.shape, -1)
y_ = y.repeat([1] + list(x.shape[1:])).reshape(*x.shape, -1).transpose(
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Rikorose/pytorch-ddtw | PairwiseDistance | false | 2,757 | [
"Apache-2.0"
] | 0 | 131d533349042a6cbcfe8b22596e12926ac7fddb | https://github.com/Rikorose/pytorch-ddtw/tree/131d533349042a6cbcfe8b22596e12926ac7fddb |
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