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 |
|---|---|---|---|---|---|---|---|---|---|---|
Learned_Aggregation_Layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mengxinpku/deit | Learned_Aggregation_Layer | false | 12,788 | [
"Apache-2.0"
] | 0 | 5b61a1ec0a4e73579f41ebdc3d34f319e5d19d14 | https://github.com/mengxinpku/deit/tree/5b61a1ec0a4e73579f41ebdc3d34f319e5d19d14 |
ScalarMix | # 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 math as tl_math
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
... | db-bionlp/CLNER | ScalarMix | false | 15,155 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
KernelSharedTensorTrain | # 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
from torch.nn import Parameter
assert_size_stride = torch._... | AndresOtero/TensorDecompositionMachineLearning | KernelSharedTensorTrain | false | 16,912 | [
"MIT"
] | 3 | 455f16b405ec9d031999b0ebf9c5a68d3c20b233 | https://github.com/AndresOtero/TensorDecompositionMachineLearning/tree/455f16b405ec9d031999b0ebf9c5a68d3c20b233 |
Swish | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
import torch.cuda
from torch.nn import Parameter
import torch.optim
class Swish(nn.Module):
def __init__(self, dim):
super(Swish, self).__init__()
self.betas = Parameter(torch.ones(dim))
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
import torch.cuda
from torch.nn import Parameter
impo... | Flamexmt/LMA | Swish | false | 13,688 | [
"MIT"
] | 321 | f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 | https://github.com/Flamexmt/LMA/tree/f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "su... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Fanzhongjie/ARFE | BalancedL1Loss | false | 435 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
HuberLoss | import torch
import torch.nn as nn
import torch.utils.data
class HuberLoss(nn.Module):
def __init__(self, delta=1):
super().__init__()
self.huber_loss_delta1 = nn.SmoothL1Loss()
self.delta = delta
def forward(self, x, x_hat):
loss = self.huber_loss_delta1(x / self.delta, x_ha... | 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
... | AndrewPaulChester/sage-code | HuberLoss | false | 20 | [
"MIT"
] | 0 | 9fe676bfbcbc6f642eca29b30a1027fba2a426a0 | https://github.com/AndrewPaulChester/sage-code/tree/9fe676bfbcbc6f642eca29b30a1027fba2a426a0 |
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.... | IAMZn1018/ccks2021-entity-linking | Attention | false | 9,110 | [
"Apache-2.0"
] | 0 | 6596b0b16d8c1fc4400c736b30ff46158d1575e4 | https://github.com/IAMZn1018/ccks2021-entity-linking/tree/6596b0b16d8c1fc4400c736b30ff46158d1575e4 |
MaxElementwise | import torch
class MaxElementwise(torch.nn.Module):
def forward(self, x, y):
return torch.max(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
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Akababa/torch2trt | MaxElementwise | false | 18,413 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
Quantization | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_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
import torch.utils.data
impo... | qwopqwop200/Fast-Invertible-Rescaling-Net | Quantization | false | 7,514 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
Linear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | cshjin/pytorch_geometric | Linear | false | 1,757 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
EncoderImagePrecomp | import torch
import numpy as np
from torch import nn
from collections import OrderedDict
import torch.nn.init
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(nn.Module):
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy as np
... | KunpengLi1994/VSRN | EncoderImagePrecomp | false | 13,962 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
CosReLU | # 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
... | fmhoward/pysurvival | CosReLU | false | 12,382 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
ODEfunc | import torch
import torch.nn as nn
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Lauu1023/torchdiffeq | ODEfunc | false | 9,356 | [
"MIT"
] | 0 | f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 | https://github.com/Lauu1023/torchdiffeq/tree/f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | HappyBelief/ContraD | ModulatedConv2d | false | 13,768 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
SelfGating | import torch
import torch.nn as nn
import torch.utils.data
import torch as th
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.cuda
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.parallel
import to... | KoDohwan/MIL-NCE_HowTo100M | SelfGating | false | 5,449 | [
"Apache-2.0"
] | 1 | 459f32b40aeb6f00da1315f957d02cd0c82f9307 | https://github.com/KoDohwan/MIL-NCE_HowTo100M/tree/459f32b40aeb6f00da1315f957d02cd0c82f9307 |
GeM | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return nn.functional.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x
.size(-1))).pow(1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__init__()
... | 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 |
LastBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | thunguyenphuoc/idinvert_pytorch | LastBlock | false | 13,133 | [
"MIT"
] | 0 | bf8a81e75d193c22a05d9c4457907dc468389766 | https://github.com/thunguyenphuoc/idinvert_pytorch/tree/bf8a81e75d193c22a05d9c4457907dc468389766 |
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.... | HusterRC/mmsegmentation | SpatialGatherModule | false | 5,310 | [
"Apache-2.0"
] | 1 | c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 | https://github.com/HusterRC/mmsegmentation/tree/c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 |
HypergradTransform | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | OliverWang-Au/learn2learn | HypergradTransform | false | 5,686 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
Discriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ajdillhoff/simgan-pytorch | Discriminator | false | 3,057 | [
"MIT"
] | 0 | fb61241a85136aae770944e1496f9319df327561 | https://github.com/ajdillhoff/simgan-pytorch/tree/fb61241a85136aae770944e1496f9319df327561 |
Quantizer | import torch
import torch.quantization
import torch.nn as nn
import torch.utils.data
class Quantizer(nn.Module):
def __init__(self):
super(Quantizer, self).__init__()
def forward(self, x, fine_tune=False):
cur_device = x.device
if self.training or fine_tune:
res = x + (to... | 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.quantization
import torch.nn as nn
import torch.utils.data
assert_... | Orange-OpenSource/AIVC | Quantizer | false | 8,632 | [
"BSD-3-Clause"
] | 18 | 8534111d1e08cdbf7efa92ebbb105af3c9044521 | https://github.com/Orange-OpenSource/AIVC/tree/8534111d1e08cdbf7efa92ebbb105af3c9044521 |
ActNormFlow | import torch
import torch.nn as nn
from typing import Tuple
class Flow(nn.Module):
def __init__(self):
super(Flow, self).__init__()
def forward(self, *inputs, **kwargs) ->Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
*inputs: input [batch, *input_size]
Returns: out... | 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
from typing import Tuple
assert_size_stride = torch... | Tiamat-Tech/VAENAR-TTS | ActNormFlow | false | 14,503 | [
"MIT"
] | 62 | 69b6b5be1ab5168cfd3c6ab902075638e76a3b8d | https://github.com/Tiamat-Tech/VAENAR-TTS/tree/69b6b5be1ab5168cfd3c6ab902075638e76a3b8d |
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... | Curli-quan/fewshot-select | PatchEmbed | false | 17,210 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | Mika412/deep-reinforcement-learning | Critic | false | 11,700 | [
"MIT"
] | 0 | 9b5ba901f760e50cd64d272939eff75881af5a9c | https://github.com/Mika412/deep-reinforcement-learning/tree/9b5ba901f760e50cd64d272939eff75881af5a9c |
InputTransition | import torch
import torch.nn as nn
def ELUCons(elu, nchan):
if elu:
return nn.ELU(inplace=True)
else:
return nn.PReLU(nchan)
class InputTransition(nn.Module):
def __init__(self, outChans, elu):
super(InputTransition, self).__init__()
self.conv1 = nn.Conv3d(1, 32, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CheerL/lancunar | InputTransition | false | 11,332 | [
"BSD-3-Clause"
] | 0 | fb00a331b5381af555fd2a7f0d03324a5355fe8c | https://github.com/CheerL/lancunar/tree/fb00a331b5381af555fd2a7f0d03324a5355fe8c |
MlpNetM | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class MlpNetM(nn.Module):
"""Implements a simple fully connected mlp network."""
def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0,
agent_shuffle='none'):
super(MlpNetM, 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
import torch.nn as nn
assert_... | HAXRD/PIC | MlpNetM | false | 8,274 | [
"MIT"
] | 28 | 658b4dd6b01e64413d5f8f0107d9167f1bd78546 | https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546 |
OneLayerFCBodyWithAction | # 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 ... | RaviTej310/mrpvf | OneLayerFCBodyWithAction | false | 11,883 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
BinaryCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import M... | HugoSenetaire/vaeac | BinaryCrossEntropyLoss | false | 13,825 | [
"MIT"
] | 70 | 451d34dd4986c52f2f37c508f03ee3db9e7408d3 | https://github.com/HugoSenetaire/vaeac/tree/451d34dd4986c52f2f37c508f03ee3db9e7408d3 |
ShuffleCatChunk | import torch
import torch.nn as nn
class ShuffleCatChunk(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
_n, c, _h, _w = a.size()
a = torch.chunk(a, chunks=c, dim=1)
b = torch.chunk(b, chunks=c, dim=1)
x = [None] * (c * 2)
x[::2] = a
x[1::2... | 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... | jjkennedy3/PINTO_model_zoo | ShuffleCatChunk | false | 6,955 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
GCN | # 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 ... | lanseyege/Graph | GCN | false | 12,694 | [
"MIT"
] | 0 | ec94502ea59d2b68de095d8160f37aa22d26f8cb | https://github.com/lanseyege/Graph/tree/ec94502ea59d2b68de095d8160f37aa22d26f8cb |
GCN | # 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... | CrowdDynamicsLab/InfoMotif | GCN | false | 17,172 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
InceptionA | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Hiroaki-Ozaki/modelib-classification | InceptionA | false | 17,407 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c |
SSDicriminatorLoss | from torch.nn import Module
import torch
from torch import Tensor
from abc import abstractmethod
from typing import Tuple
import torch.nn as nn
from typing import Dict
import torch.utils.data.distributed
from torch.nn import CrossEntropyLoss
from torch.backends import cudnn as cudnn
from torch.nn import BCELoss
class... | 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.... | MIPT-Oulu/Collagen | SSDicriminatorLoss | false | 17,663 | [
"MIT"
] | 4 | 0cbc4285d60e5c9fcc89f629fcf4321e80b7452c | https://github.com/MIPT-Oulu/Collagen/tree/0cbc4285d60e5c9fcc89f629fcf4321e80b7452c |
EPE | # 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_... | Egazaga/arXiv2020-RIFE | EPE | false | 393 | [
"MIT"
] | 0 | 84b050e168a682905a9dde8aa15437a4994f2abf | https://github.com/Egazaga/arXiv2020-RIFE/tree/84b050e168a682905a9dde8aa15437a4994f2abf |
DiceBCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor',
smooth: 'int'=1):
inputs = input... | 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... | Latterlig96/DCUnet | DiceBCELoss | false | 8,456 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
GenerationProbabilty | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | abhishek0318/conll-sigmorphon-2018 | GenerationProbabilty | false | 18,208 | [
"MIT"
] | 6 | de4b8da7778947e03e7a35b56e0e53281f65e403 | https://github.com/abhishek0318/conll-sigmorphon-2018/tree/de4b8da7778947e03e7a35b56e0e53281f65e403 |
RWKV_ChannelMix | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | JunnYu/Paddle-AI-Writer | RWKV_ChannelMix | false | 8,795 | [
"BSD-3-Clause"
] | 25 | 8d211f9e60aeed323b6330065668f54350514c70 | https://github.com/JunnYu/Paddle-AI-Writer/tree/8d211f9e60aeed323b6330065668f54350514c70 |
State_Autoencoder | import torch
import torch.nn as nn
from collections import OrderedDict
class State_Autoencoder(nn.Module):
def __init__(self, frame_stacks=1, channels=3):
super(State_Autoencoder, self).__init__()
self.encoder = nn.Sequential(OrderedDict([('encoder_conv1', nn.
Conv2d(channels * frame_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Squishy123/GDE_net | State_Autoencoder | false | 17,951 | [
"Apache-2.0"
] | 4 | 9094cbf58edbf0d62a2b2cd66743322597f66269 | https://github.com/Squishy123/GDE_net/tree/9094cbf58edbf0d62a2b2cd66743322597f66269 |
Discriminator | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Discriminator, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Dora-The-Kid/culture_network | Discriminator | false | 2,182 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
MultiHeadedAttention | # 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.... | WenjingXia/wenet | MultiHeadedAttention | false | 1,244 | [
"Apache-2.0"
] | 0 | 9a1fd005cd06be16518a5476076b2ae6af2ec41a | https://github.com/WenjingXia/wenet/tree/9a1fd005cd06be16518a5476076b2ae6af2ec41a |
SoftmaxModel | import torch
import torch.nn as nn
class SoftmaxModel(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out, inplace=False) ->None:
super().__init__()
self.num_in = num_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | LMdeLiangMi/captum | SoftmaxModel | false | 5,476 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.a1 = nn.Conv2d(19, 64, kernel_size=3, padding=1)
self.a2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.a3 = nn.Conv2d(128, 256, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | afozk95/chess-dataset | Net | false | 12,081 | [
"MIT"
] | 0 | 08de7b251f67cb8553a5ee66f6fd76cefeb14bb4 | https://github.com/afozk95/chess-dataset/tree/08de7b251f67cb8553a5ee66f6fd76cefeb14bb4 |
HadamardProduct | # 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... | KimUyen/LSTM-BCI-Decoder | HadamardProduct | false | 8,395 | [
"MIT"
] | 38 | c7b4bd108335a4d6c7d99c00c263346026186b0b | https://github.com/KimUyen/LSTM-BCI-Decoder/tree/c7b4bd108335a4d6c7d99c00c263346026186b0b |
TransposedConv1d | # 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 ... | TencentYoutuResearch/ActionDetection-AFSD | TransposedConv1d | false | 14,469 | [
"BSD-3-Clause"
] | 112 | ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 | https://github.com/TencentYoutuResearch/ActionDetection-AFSD/tree/ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 |
GaussianSmearing | # 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... | Open-Catalyst-Project/baselines | GaussianSmearing | false | 17,802 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | alhsu713/fast_blind_video_consistency | ResidualBlock | false | 12,078 | [
"MIT"
] | 0 | 2037ec5f68a361b926c31b3a12c1cd04e2331797 | https://github.com/alhsu713/fast_blind_video_consistency/tree/2037ec5f68a361b926c31b3a12c1cd04e2331797 |
MaxPool3x3 | # 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... | KelvinYang0320/nas-without-training | MaxPool3x3 | false | 13,932 | [
"MIT"
] | 385 | 5ed77a06726a73233a5a93b8f70a7172ce570029 | https://github.com/KelvinYang0320/nas-without-training/tree/5ed77a06726a73233a5a93b8f70a7172ce570029 |
FM | import torch
import torch.nn as nn
from sklearn.metrics import *
class FM(nn.Module):
"""Factorization Machine models pairwise (order-2) feature interactions
without linear term and bias.
Input shape
- 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Sunmyunghan/Final_Project | FM | false | 1,117 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
ToyNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ToyNet(nn.Module):
def __init__(self):
super(ToyNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.conv3 = nn.Conv2d(16, 64, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | asalmanp/MIVisionX | ToyNet | false | 15,028 | [
"MIT"
] | 153 | a964774944331827c8d6e9bb1ffbb2578f335056 | https://github.com/asalmanp/MIVisionX/tree/a964774944331827c8d6e9bb1ffbb2578f335056 |
BothContextGate | 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ChenRocks/Distill-BERT-Textgen-ONMT | BothContextGate | false | 17,096 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
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
import torch
a... | ZijiaLewisLu/action-recognition-pytorch | TemporalPooling | false | 14,726 | [
"Apache-2.0"
] | 149 | 6ee04ed249081eb0d8e1b4a3e7a5c11fa65b8d70 | https://github.com/ZijiaLewisLu/action-recognition-pytorch/tree/6ee04ed249081eb0d8e1b4a3e7a5c11fa65b8d70 |
CategoricalAccuracy | # 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
assert_size_stride = t... | HalleyYoung/MusicTransformer-pytorch | CategoricalAccuracy | false | 11,467 | [
"MIT"
] | 0 | bbfb7050f4a81675b089cd826d4476cf29bf19c2 | https://github.com/HalleyYoung/MusicTransformer-pytorch/tree/bbfb7050f4a81675b089cd826d4476cf29bf19c2 |
RobertaClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AkariAsai/logic_guided_qa | RobertaClassificationHead | false | 14,568 | [
"MIT"
] | 69 | 96ae70f01b7267ef0b472b8497c903035d052fd9 | https://github.com/AkariAsai/logic_guided_qa/tree/96ae70f01b7267ef0b472b8497c903035d052fd9 |
ChannelSELayer | # 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 ... | evdcush/ppuda | ChannelSELayer | false | 15,360 | [
"MIT"
] | 262 | 22783ac92207da6730ee618c953af230c5c39f28 | https://github.com/evdcush/ppuda/tree/22783ac92207da6730ee618c953af230c5c39f28 |
PointerNetwork | # 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.... | tailerr/R-NET-pytorch | PointerNetwork | false | 4,412 | [
"MIT"
] | 0 | a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd | https://github.com/tailerr/R-NET-pytorch/tree/a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd |
DPLSTMCell | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the gra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ffuuugor/opacus | DPLSTMCell | false | 6,695 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
BackboneModel1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | HarshCasper/nni | BackboneModel1 | false | 5,275 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
VAE | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.autograd
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Li... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | HolyLow/examples | VAE | false | 11,514 | [
"BSD-3-Clause"
] | 0 | 23b0cb1022cf7a21428883e95fded01d74a059bf | https://github.com/HolyLow/examples/tree/23b0cb1022cf7a21428883e95fded01d74a059bf |
Biaffine | # 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... | LindgeW/BiaffineNER | Biaffine | false | 8,471 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
TverskyLoss | import torch
from torch import nn
class TverskyLoss(nn.Module):
"""DiceLoss implemented from 'Dice Loss for Data-imbalanced NLP Tasks'
Useful in dealing with unbalanced data
Add softmax automatically
"""
def __init__(self):
super(TverskyLoss, self).__init__()
self.m = nn.Sigmoid()... | 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... | ZhaoZhibin/Physionet2020model | TverskyLoss | false | 18,171 | [
"BSD-2-Clause",
"MIT"
] | 6 | ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e | https://github.com/ZhaoZhibin/Physionet2020model/tree/ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e |
Conv2d | import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
relu=True, same_padding=False, bn=False):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding else 0
self.conv = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ChrisKonishi/multi-stream-crowd-counting-extended | Conv2d | false | 262 | [
"MIT"
] | 0 | 4b1590499bd93ac09e62c4c7760b88ae92e6b301 | https://github.com/ChrisKonishi/multi-stream-crowd-counting-extended/tree/4b1590499bd93ac09e62c4c7760b88ae92e6b301 |
BranchNet | # 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_... | CNN-NISER/lffd-pytorch | BranchNet | false | 13,448 | [
"MIT"
] | 220 | 7d6476ece79cf75c6265c89346ddac48929ce8f6 | https://github.com/CNN-NISER/lffd-pytorch/tree/7d6476ece79cf75c6265c89346ddac48929ce8f6 |
ResNetBlock | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import InstanceNorm2d
from torch.nn.init import kaiming_normal_
from torch.nn.init import xavier_normal_
from torch import relu
def create_init_function(method: 'str'='none'):
def init(module: 'Module'):
if method == 'none... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | kongdongdien/talking-head-anime-demo | ResNetBlock | false | 15,858 | [
"MIT"
] | 1,670 | d66c27a341f7256e4a37c55493b93dc9e846b423 | https://github.com/kongdongdien/talking-head-anime-demo/tree/d66c27a341f7256e4a37c55493b93dc9e846b423 |
TilePad2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class TilePad2d(nn.Module):
def __init__(self, left, right, top, bottom):
super().__init__()
self.left = left
self.right = right
self.top = top
self.bottom = bottom
def forward(self, x):
return... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | mkarmann/conway-reversed | TilePad2d | false | 10,672 | [
"MIT"
] | 0 | a3ae10dd5768affb9caf193a246395ee0fb2bc6f | https://github.com/mkarmann/conway-reversed/tree/a3ae10dd5768affb9caf193a246395ee0fb2bc6f |
MaxElementwise | import torch
class MaxElementwise(torch.nn.Module):
def forward(self, x, y):
return torch.max(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
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Ilyabasharov/torch2trt | MaxElementwise | false | 2,523 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
LeNet_300_100 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet_300_100(nn.Module):
"""Simple NN with hidden layers [300, 100]
Based on https://github.com/mi-lad/snip/blob/master/train.py
by Milad Alizadeh.
"""
def __init__(self, save_features=None, bench_model=False):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | VITA-Group/SViTE | LeNet_300_100 | false | 14,553 | [
"MIT"
] | 50 | b0c62fd153c8b0b99917ab935ee76925c9de1149 | https://github.com/VITA-Group/SViTE/tree/b0c62fd153c8b0b99917ab935ee76925c9de1149 |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | FengNiMa/pytorch_diffusion_model_celebahq | Upsample | false | 8,107 | [
"MIT"
] | 17 | b81e57453066e05d71feb8451bbff766df401386 | https://github.com/FengNiMa/pytorch_diffusion_model_celebahq/tree/b81e57453066e05d71feb8451bbff766df401386 |
Hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import product as product
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from itertools import product as product
assert_size_stride = torch... | PoWeiChiao/3DDFA_V2 | Hswish | false | 11,784 | [
"MIT"
] | 0 | 5b4ae883705a1f5b1f15c19203bedbd47fc8a832 | https://github.com/PoWeiChiao/3DDFA_V2/tree/5b4ae883705a1f5b1f15c19203bedbd47fc8a832 |
ConvBlockINE | import torch
from torch import nn
from torch.nn import init as init
class ConvBlockINE(nn.Module):
def __init__(self, in_ch, out_ch, act='relu', ksize=3):
super().__init__()
padding = (ksize - 1) // 2
if act == 'lrelu':
self.act = nn.LeakyReLU(0.2, True)
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BaekduChoi/Halftoning_v2 | ConvBlockINE | false | 2,048 | [
"BSD-3-Clause"
] | 0 | fdb7040e1a4044f23ef9c92757bbb90c23685afe | https://github.com/BaekduChoi/Halftoning_v2/tree/fdb7040e1a4044f23ef9c92757bbb90c23685afe |
Encoding | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (bat... | 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
... | Atten4Vis/DemystifyLocalViT | Encoding | false | 13,365 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
CustomBatchNormManualModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | akashrajkn/sarcastic-gradients | CustomBatchNormManualModule | false | 3,067 | [
"Apache-2.0"
] | 0 | 5a995ab7822dfd49cdc88855c631dcc8f1b0532f | https://github.com/akashrajkn/sarcastic-gradients/tree/5a995ab7822dfd49cdc88855c631dcc8f1b0532f |
softmax_SR | # 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
... | CILAB-MA/Machine_ToM | softmax_SR | false | 7,843 | [
"MIT"
] | 13 | 8c168ee31cc95a7f57998e8907273799533fe04f | https://github.com/CILAB-MA/Machine_ToM/tree/8c168ee31cc95a7f57998e8907273799533fe04f |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(IoULoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = F.sigmoid(inputs)
inputs = inputs.view(-1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DoggyLiu0116/MamboNet | IoULoss | false | 5,071 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
GraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
import torch.autograd
assert_size_stride = ... | Mason-McGough/kaolin | GraphConv | false | 2,639 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 2b628842cda7dac7452eedcf05881849a38b90b1 | https://github.com/Mason-McGough/kaolin/tree/2b628842cda7dac7452eedcf05881849a38b90b1 |
SqueezeExcite | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | wangxianliang/FaceX-Zoo | SqueezeExcite | false | 13,091 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
Discriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | dendisuhubdy/pytorch_geometric | Discriminator | false | 1,825 | [
"MIT"
] | 0 | a0592f61aef617c0c8ff61b3d822d04901054c22 | https://github.com/dendisuhubdy/pytorch_geometric/tree/a0592f61aef617c0c8ff61b3d822d04901054c22 |
GlobalMaxPool1d | import torch
from torch import nn
class GlobalMaxPool1d(nn.Module):
"""Performs global max pooling over the entire length of a batched 1D tensor
# Arguments
input: Input tensor
"""
def forward(self, input):
return nn.functional.max_pool1d(input, kernel_size=input.size()[2:]
... | 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... | liaoweiduo/few-shot | GlobalMaxPool1d | false | 12,701 | [
"MIT"
] | 0 | 24d54fa3b472194b8cdab0ec6017bc5f649380a0 | https://github.com/liaoweiduo/few-shot/tree/24d54fa3b472194b8cdab0ec6017bc5f649380a0 |
SimpleNet | # 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.cuda
import torch.backends.cudnn
import torch.backends.mkl
assert_s... | JudeDavis1/intel-extension-for-pytorch | SimpleNet | false | 2,590 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
LinearDiag | import torch
import torch.nn as nn
import torch.optim
import torch.nn.parallel
class LinearDiag(nn.Module):
def __init__(self, num_features, bias=False):
super(LinearDiag, self).__init__()
weight = torch.FloatTensor(num_features).fill_(1)
self.weight = nn.Parameter(weight, requires_grad=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
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | nikran1/Few_shot | LinearDiag | false | 16,173 | [
"MIT"
] | 497 | 5298c98e208411e44ee7767e6f4d457006d373cb | https://github.com/nikran1/Few_shot/tree/5298c98e208411e44ee7767e6f4d457006d373cb |
Shift | # 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... | CPJKU/kagglebirds2020 | Shift | false | 17,043 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
ScaledSiLU | import torch
class ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | RolnickLab/ocp | ScaledSiLU | false | 2,769 | [
"MIT"
] | 0 | e120c3b90203a46f5fc7626f0b5c8979e4944765 | https://github.com/RolnickLab/ocp/tree/e120c3b90203a46f5fc7626f0b5c8979e4944765 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | chagmgang/object_relation_transformer | LanguageModelCriterion | false | 6,409 | [
"MIT"
] | 1 | 04b88514f97232c12b576720e4b82226751c3c48 | https://github.com/chagmgang/object_relation_transformer/tree/04b88514f97232c12b576720e4b82226751c3c48 |
DownsampleA | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | YasufumiSakai/Pruning | DownsampleA | false | 6,007 | [
"BSD-3-Clause"
] | 1 | 5c8bc0d780fab41e1bd894b0360bd50e14cd0571 | https://github.com/YasufumiSakai/Pruning/tree/5c8bc0d780fab41e1bd894b0360bd50e14cd0571 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma):
super().__init__()
self.gamma = gamma
def forward(self, input, target):
if not target.size() == input.size():
raise ValueError(
'Targe... | 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... | xkp793003821/kaggle-tgs-salt | FocalLoss | false | 4,584 | [
"MIT"
] | 0 | 4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 | https://github.com/xkp793003821/kaggle-tgs-salt/tree/4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 |
Qux | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | Qux | false | 14,638 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
LogisticRegression | # 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.... | Yalfoosh/DUBUCE | LogisticRegression | false | 1,256 | [
"Apache-2.0"
] | 0 | 3f53923c27b1bce0ac592b20c5bb98649cb7fb75 | https://github.com/Yalfoosh/DUBUCE/tree/3f53923c27b1bce0ac592b20c5bb98649cb7fb75 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_pt... | ShowLo/Networks | Swish | false | 1,056 | [
"MIT"
] | 0 | 48f8545783966c383b6c3b600fbe37a15ea8ae3c | https://github.com/ShowLo/Networks/tree/48f8545783966c383b6c3b600fbe37a15ea8ae3c |
Vflip | import torch
import torch.nn as nn
def vflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2])
class Vflip(nn.Module):
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | ChristophReich1996/kornia | Vflip | false | 284 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
QuantizableHSigmoid | import torch
import torch.nn as nn
import torch.quantization
class QuantizableHSigmoid(nn.Module):
"""Hard Sigmoid for quantization."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(QuantizableHSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | HwangJohn/model_compression | QuantizableHSigmoid | false | 13,818 | [
"MIT"
] | 216 | 1df40c8a531313cc9e79255f4477f39d66d9b849 | https://github.com/HwangJohn/model_compression/tree/1df40c8a531313cc9e79255f4477f39d66d9b849 |
MultiLabelDiceLoss | # 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... | jchen42703/reproducing-cloud-3rd-place | MultiLabelDiceLoss | false | 6,937 | [
"Apache-2.0"
] | 1 | 25571f53efd48f68735d7fe2991e3ad783cbd4b1 | https://github.com/jchen42703/reproducing-cloud-3rd-place/tree/25571f53efd48f68735d7fe2991e3ad783cbd4b1 |
LuongAttentionGeneral | # 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.... | beroguedou/nmt-pytorch | LuongAttentionGeneral | false | 6,341 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
WeightedCE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | devaansh100/pytorch_connectomics | WeightedCE | false | 6,569 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
FCNet | import torch
import torch.nn.functional
from torch import nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | rafiberlin/clp-sose21-pm-vision | FCNet | false | 4,161 | [
"MIT"
] | 0 | 55c786182ed4568cdeda4bb3676fa02b9580d68d | https://github.com/rafiberlin/clp-sose21-pm-vision/tree/55c786182ed4568cdeda4bb3676fa02b9580d68d |
WBCEDiceLoss | # 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
... | Hhhhhhhhhhao/change_detection | WBCEDiceLoss | false | 8,234 | [
"MIT"
] | 11 | 13b87c02166cc98d39d8be240a07abcf12893fe3 | https://github.com/Hhhhhhhhhhao/change_detection/tree/13b87c02166cc98d39d8be240a07abcf12893fe3 |
SummaryNet_large | import torch
import torch.nn as nn
import torch.nn.functional as F
class SummaryNet_large(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv1d(in_channels=2, out_channels=20, kernel_size=
3, padding=2)
self.pool = nn.MaxPool1d(kernel_size=5, stride=5)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | Wrede/BNN-LFI | SummaryNet_large | false | 2,971 | [
"MIT"
] | 0 | 8c5094f01c1eef286bdd84613c7259d534d2eb7e | https://github.com/Wrede/BNN-LFI/tree/8c5094f01c1eef286bdd84613c7259d534d2eb7e |
KLLoss | # 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... | ModelTC/EOD | KLLoss | false | 14,082 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
SimMinLoss | # 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
assert_size_stride = t... | CVI-SZU/CLIMS | SimMinLoss | false | 17,048 | [
"MIT"
] | 4 | 9d3d0123b625b2c6941069e8fb359019a5cabd59 | https://github.com/CVI-SZU/CLIMS/tree/9d3d0123b625b2c6941069e8fb359019a5cabd59 |
TransformerEncoderLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def _normalize(tensor, norm_layer):
"""
Broadcast layer norm
"""
size = tensor.size()
return norm_layer(tensor.view(-1, size[-1])).view(size)
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, 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.... | Guaguago/Persona-Dialogue-Generation | TransformerEncoderLayer | false | 13,736 | [
"MIT"
] | 258 | 0d4526ec8eddff62751a70666e14d72103906f44 | https://github.com/Guaguago/Persona-Dialogue-Generation/tree/0d4526ec8eddff62751a70666e14d72103906f44 |
BBoxTransform | # 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
import torch.nn.parallel
import torch.optim
import ... | NHERI-SimCenter/BRAILS | BBoxTransform | false | 8,579 | [
"BSD-3-Clause"
] | 22 | ec17bcd000b15cb8c2933728fe2fd1fb190cd852 | https://github.com/NHERI-SimCenter/BRAILS/tree/ec17bcd000b15cb8c2933728fe2fd1fb190cd852 |
EfficientBaseQuantization | import torch
import numpy as np
from torch import nn
class _EfficientBaseQuantizationFunction(torch.autograd.Function):
@staticmethod
def clip(x, min_value, max_value):
x = torch.min(x, max_value)
x = torch.max(x, min_value)
return x
@staticmethod
def forward(ctx, x, delta, q... | 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 numpy as np
from torc... | UniSerj/ai-research | EfficientBaseQuantization | false | 14,532 | [
"Apache-2.0"
] | 46 | 79f0093c93408cc5dd7d3f56aafd7dc1f901421c | https://github.com/UniSerj/ai-research/tree/79f0093c93408cc5dd7d3f56aafd7dc1f901421c |
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