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 |
|---|---|---|---|---|---|---|---|---|---|---|
PairwiseNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PairwiseNetwork(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.fc1 = nn.Linear(hidden_size, 2 * hidden_size)
self.fc2 = nn.Linear(2 * hidden_size, hidden_size)
self.fc3 = nn.Linear(hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | GrantXie/wikidata-wikifier | PairwiseNetwork | false | 17,307 | [
"MIT"
] | 3 | a65c9b71596e390999af9de7638eb8c8c13c1581 | https://github.com/GrantXie/wikidata-wikifier/tree/a65c9b71596e390999af9de7638eb8c8c13c1581 |
IRevInjectivePad | import torch
import torch.utils.data
import torch.nn as nn
class IRevInjectivePad(nn.Module):
"""
i-RevNet channel zero padding block.
Parameters:
----------
padding : int
Size of the padding.
"""
def __init__(self, padding):
super(IRevInjectivePad, 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
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | HyperGAN/imgclsmob | IRevInjectivePad | false | 17,673 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
RegLoss | # 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_... | MIracleyin/RecBole-notebook | RegLoss | false | 9,563 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
DQN_RAM | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN_RAM(nn.Module):
def __init__(self, in_features=4, num_actions=18):
"""
Initialize a deep Q-learning network for testing algorithm
in_features: number of features of input.
num_actions: number of a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | paulesta55/pytorch-dqn | DQN_RAM | false | 12,866 | [
"MIT"
] | 0 | 0c1345952c8f99b2f74ec357867262fae6d928ec | https://github.com/paulesta55/pytorch-dqn/tree/0c1345952c8f99b2f74ec357867262fae6d928ec |
FCDiscriminator_low | # 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... | seabearlmx/PA-DAN | FCDiscriminator_low | false | 4,298 | [
"MIT"
] | 0 | bdd1200396d102e68acdd265db9d22ddb83b6404 | https://github.com/seabearlmx/PA-DAN/tree/bdd1200396d102e68acdd265db9d22ddb83b6404 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | mkoivi-ms/unet-pytorch-azureml | DiceLoss | false | 16,095 | [
"MIT"
] | 517 | f0fa5b15cfad19f6b04bb309a965726c25c39e03 | https://github.com/mkoivi-ms/unet-pytorch-azureml/tree/f0fa5b15cfad19f6b04bb309a965726c25c39e03 |
判断状态 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | chenjun-110/WZCQ | 判断状态 | false | 1,683 | [
"Apache-2.0"
] | 0 | e2de7743ad671e8632cfa084638555d7f1deb42f | https://github.com/chenjun-110/WZCQ/tree/e2de7743ad671e8632cfa084638555d7f1deb42f |
FGFunction | # 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... | Myyyr/segmentation | FGFunction | false | 868 | [
"MIT"
] | 0 | 6b9423e327cff1eb23599404031b7fb8e9ecf75d | https://github.com/Myyyr/segmentation/tree/6b9423e327cff1eb23599404031b7fb8e9ecf75d |
InceptionC | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Galaxies99/inception-cuda | InceptionC | false | 11,446 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
ReferenceWeightBinarizationModule | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import *
import torch.onnx
class ReferenceDOREFABinarize(torch.autograd.Function):
@staticmethod
def f... | 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
from torchvision import models as models
import torc... | aalborov/openvino_training_extensions | ReferenceWeightBinarizationModule | false | 6,033 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
ConcatSquashLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ClaraBing/ffjord | ConcatSquashLinear | false | 13,504 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
ConvolutionModule | import torch
from torch import Tensor
from torch import nn
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: 'Tensor') ->Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
class ConvolutionModule(nn.Module):
"""ConvolutionModule... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 T... | huangruizhe/icefall | ConvolutionModule | false | 15,554 | [
"Apache-2.0"
] | 173 | ea8af0ee9af5169d93f8f389ffebbc27a1d9e82a | https://github.com/huangruizhe/icefall/tree/ea8af0ee9af5169d93f8f389ffebbc27a1d9e82a |
DeConvNet2 | # 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_... | Neural-Diffusion-Research/normalized-autoencoders | DeConvNet2 | false | 8,637 | [
"MIT"
] | 30 | 0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 | https://github.com/Neural-Diffusion-Research/normalized-autoencoders/tree/0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 |
FusedUpsample | import torch
import torch.nn.functional as F
from torch import nn
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | uzielroy/StyleGan_FewShot | FusedUpsample | false | 16,645 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
CNNCifar | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
from torch import nn
class CNNCifar(nn.Module):
def __init__(self, args):
super(CNNCifar, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = 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
from torch._inductor.runtime.... | ITSEG-MQ/Chain-PPFL | CNNCifar | false | 18,369 | [
"MIT"
] | 8 | 21d4fafcd8e118cc4eaa35348f1204fecce78138 | https://github.com/ITSEG-MQ/Chain-PPFL/tree/21d4fafcd8e118cc4eaa35348f1204fecce78138 |
InvertibleDownsampling1D | # 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.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleDownsampling1D | false | 15,032 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
MultiheadAttention | import math
import torch
import torch.nn as nn
import torch as th
class QKVMultiheadAttention(nn.Module):
def __init__(self, n_heads: 'int', n_ctx: 'int'):
super().__init__()
self.n_heads = n_heads
self.n_ctx = n_ctx
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dbanys/glide-text2im | MultiheadAttention | false | 3,402 | [
"MIT"
] | 0 | 5177545ec62f1fddc3075a8a69b63df3eb2256a5 | https://github.com/dbanys/glide-text2im/tree/5177545ec62f1fddc3075a8a69b63df3eb2256a5 |
MinLossModule | # 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... | pkalluri/specialized-conditional-pcnn | MinLossModule | false | 4,121 | [
"Apache-2.0"
] | 0 | ed94e47654ed749a7dd3492c4e074e2a8fb12df8 | https://github.com/pkalluri/specialized-conditional-pcnn/tree/ed94e47654ed749a7dd3492c4e074e2a8fb12df8 |
SimpleSoftPlusModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size... | YaronBenAtar/glow | SimpleSoftPlusModel | false | 14,691 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
EncoderLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch.nn import functional as F
from torch.autograd import Variable
from torch import nn
def softmax(x):
if x.dim() == 3:
return F.softmax(x.transpose(0, 2)).transpose(0, 2)
return F.softmax(x)
def gumbel_softmax(input, beta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | cclauss/nonauto-nmt | EncoderLayer | false | 15,022 | [
"BSD-3-Clause"
] | 262 | efcbe4f2329b140ac3ce06abb6409457cebc8e49 | https://github.com/cclauss/nonauto-nmt/tree/efcbe4f2329b140ac3ce06abb6409457cebc8e49 |
ScaleDotProductAttention | import math
import torch
import torch.nn as nn
class ScaleDotProductAttention(nn.Module):
"""
compute scale dot product attention
Query : given sentence that we focused on (decoder)
Key : every sentence to check relationship with Qeury(encoder)
Value : every sentence same with Key (encoder)
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | jkimbf/transformer-1 | ScaleDotProductAttention | false | 15,717 | [
"Apache-2.0"
] | 233 | 6cd29731197822d6db641cdbfad3b045b8a294e4 | https://github.com/jkimbf/transformer-1/tree/6cd29731197822d6db641cdbfad3b045b8a294e4 |
AffineConstantFlow | import torch
from torch import nn
class AffineConstantFlow(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super().__... | 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_... | ilkhem/icebeem | AffineConstantFlow | false | 15,591 | [
"MIT"
] | 48 | 0077f0120c83bcc6d9b199b831485c42bed2401f | https://github.com/ilkhem/icebeem/tree/0077f0120c83bcc6d9b199b831485c42bed2401f |
Out | import torch
from torch import nn
class Out(nn.Module):
def forward(self, out):
out_std = torch.sqrt(out.var(0, unbiased=False) + 1e-08)
mean_std = out_std.mean()
mean_std = mean_std.expand(out.size(0), 1, 4, 4)
out = torch.cat((out, mean_std), 1)
return out
def get_inpu... | 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... | nazarblch/style-based-gan-pytorch | Out | false | 4,049 | [
"MIT"
] | 0 | 5ed7fa114904501d77b414921cd9f439773ba24c | https://github.com/nazarblch/style-based-gan-pytorch/tree/5ed7fa114904501d77b414921cd9f439773ba24c |
Conv2d_GN_ReLU | # 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.... | FANG-Xiaolin/uois | Conv2d_GN_ReLU | false | 2,220 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
SelfAttention | # 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.... | YapingZ/News-image-caption | SelfAttention | false | 3,012 | [
"Apache-2.0"
] | 0 | fcccf51bbe5607adbf71c1da8ecdc6693555993f | https://github.com/YapingZ/News-image-caption/tree/fcccf51bbe5607adbf71c1da8ecdc6693555993f |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | 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... | AllenPeng0209/SaccadeNet | GHMR | false | 7,660 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
HammingLoss | # 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... | uclnlp/torch-imle | HammingLoss | false | 16,640 | [
"MIT"
] | 205 | f595cd8d527466f6b5db79276f6ceee01d100a1c | https://github.com/uclnlp/torch-imle/tree/f595cd8d527466f6b5db79276f6ceee01d100a1c |
BERTIntermediate | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | BingzhangZhu/Covid19-ABSA | BERTIntermediate | false | 8,785 | [
"MIT"
] | 31 | e488e74ee53882bba56aedfafb3846ab82c4678e | https://github.com/BingzhangZhu/Covid19-ABSA/tree/e488e74ee53882bba56aedfafb3846ab82c4678e |
NextSentencePrediction | import torch
import torch.nn as nn
from itertools import chain as chain
import torch.utils.data
import torch.hub
import torch.nn.parallel
import torch.optim
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, 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
from torch._inductor.runtime.... | byeongjokim/LateTemporalModeling3DCNN_for_sign | NextSentencePrediction | false | 1,628 | [
"MIT"
] | 0 | e3a802fcf91dc3930aea782464ee34d9b747d3ab | https://github.com/byeongjokim/LateTemporalModeling3DCNN_for_sign/tree/e3a802fcf91dc3930aea782464ee34d9b747d3ab |
ReluLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torchvision.models._utils import IntermediateLayerGetter as In... | Cospel/facexlib | ReluLayer | false | 9,418 | [
"MIT"
] | 0 | 2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa | https://github.com/Cospel/facexlib/tree/2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa |
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... | jyhong836/pytorch-dp | SequenceBias | false | 10,346 | [
"Apache-2.0"
] | 0 | e050b98d630d4db50cacc4fff82575daf345f012 | https://github.com/jyhong836/pytorch-dp/tree/e050b98d630d4db50cacc4fff82575daf345f012 |
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Justin900429/vision-transformer | PatchEmbed | false | 5,446 | [
"MIT"
] | 1 | e149092efbb83c166449944137db0ee5200f9325 | https://github.com/Justin900429/vision-transformer/tree/e149092efbb83c166449944137db0ee5200f9325 |
Prototypes | import torch
import torch.nn as nn
from torch.nn import functional as F
class Prototypes(nn.Module):
def __init__(self, fdim, num_classes, temp=0.05):
super().__init__()
self.prototypes = nn.Linear(fdim, num_classes, bias=False)
self.temp = temp
def forward(self, x):
x = F.no... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Baymine/Dassl | Prototypes | false | 11,244 | [
"MIT"
] | 0 | 0836fb1f08393e2204326618e783d796741f657e | https://github.com/Baymine/Dassl/tree/0836fb1f08393e2204326618e783d796741f657e |
ClippedLinearQuantization | # 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
from torch.optim.lr_schedule... | HatsuneMiku4/distiller | ClippedLinearQuantization | false | 2,390 | [
"Apache-2.0"
] | 0 | 8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 | https://github.com/HatsuneMiku4/distiller/tree/8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 |
LinearClassifier | # 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_... | Jincheng-Sun/Kylearn-pytorch | LinearClassifier | false | 663 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
BasicModel3 | # 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... | ngduduong/captum | BasicModel3 | false | 4,070 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Mukosame/AODA | FocalLoss | false | 8,587 | [
"BSD-3-Clause"
] | 43 | c187e5ff0a6502a9166da37a213ee259afa60903 | https://github.com/Mukosame/AODA/tree/c187e5ff0a6502a9166da37a213ee259afa60903 |
Hsigmoid | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards... | FluteXu/DW-Research | Hsigmoid | false | 13,689 | [
"Apache-2.0"
] | 780 | 6b559d2d1d440c07e5936a65cd74a3bc657962dc | https://github.com/FluteXu/DW-Research/tree/6b559d2d1d440c07e5936a65cd74a3bc657962dc |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Ago3/VLP | RewardCriterion | false | 8,829 | [
"Apache-2.0"
] | 0 | 4dec0e04b8592f4a74fe66c253dbb92574e7e2ba | https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba |
PositionalScaledDotProductAttention | # 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.... | TomerRonen34/MeshCNN | PositionalScaledDotProductAttention | false | 5,905 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
Network | import torch
class Network(torch.nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 64, kernel_size=5)
self.conv2 = torch.nn.Conv2d(64, 512, kernel_size=5)
self.fc1 = torch.nn.Linear(2048, 256)
self.fc2 = torch.nn.Linear(256, 12... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AbrahamAcosta/leaves_cnn | Network | false | 11,178 | [
"MIT"
] | 0 | e6be28ef696dc427aa50c7d4581a29a05d1e7a94 | https://github.com/AbrahamAcosta/leaves_cnn/tree/e6be28ef696dc427aa50c7d4581a29a05d1e7a94 |
LinearMaxPoolLinearModel | import torch
import torch.nn as nn
class LinearMaxPoolLinearModel(nn.Module):
def __init__(self) ->None:
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | LMdeLiangMi/captum | LinearMaxPoolLinearModel | false | 5,483 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
PoseNormalize | # 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... | chinitaberrio/DeepPrivacy | PoseNormalize | false | 15,026 | [
"MIT"
] | 1,128 | d50e1b5ae762b47ab5a8f54cb90e66465057bd25 | https://github.com/chinitaberrio/DeepPrivacy/tree/d50e1b5ae762b47ab5a8f54cb90e66465057bd25 |
SamePadConv2d | # 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... | DandelionLau/NetworkCollections | SamePadConv2d | false | 17,218 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 |
SILogLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.hub
class SILogLoss(nn.Module):
def __init__(self):
super(SILogLoss, self).__init__()
self.name = 'SILog'
def forward(self, input, target, mask=None, interpolate=False):
... | 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... | appliedinnovation/fast-depth | SILogLoss | false | 1,459 | [
"MIT"
] | 0 | 4606b4d340ae416de94afed45bc767fe6f64bd67 | https://github.com/appliedinnovation/fast-depth/tree/4606b4d340ae416de94afed45bc767fe6f64bd67 |
ToRGB | from torch.autograd import Function
import math
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 FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.nn as nn
import tor... | AsianZeus/Diverse-Facial-Edit | ToRGB | false | 9,428 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
StableBCELoss | import torch
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_in... | 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... | CarlosPena00/pytorch-unet | StableBCELoss | false | 203 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
NormalLikelihood | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
class NormalLikelihood(nn.Module):
def __init__(self):
super(NormalLikelihoo... | 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
... | CrispyHarder/ppuda | NormalLikelihood | false | 816 | [
"MIT"
] | 0 | 15950ba297188163eaadd8ab69268ee7f6ffcf2a | https://github.com/CrispyHarder/ppuda/tree/15950ba297188163eaadd8ab69268ee7f6ffcf2a |
M | # 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.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | lenaguignard/examples | M | false | 15,889 | [
"BSD-3-Clause"
] | 19,783 | 973e77b725a6028289a90170f0b237ea2e71d4f2 | https://github.com/lenaguignard/examples/tree/973e77b725a6028289a90170f0b237ea2e71d4f2 |
ExponentialEnvelope | import torch
class ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Irlirion/ocp | ExponentialEnvelope | false | 13,837 | [
"MIT",
"BSD-3-Clause"
] | 242 | 6fb3e794eef31559db990300198eca20f41d8f37 | https://github.com/Irlirion/ocp/tree/6fb3e794eef31559db990300198eca20f41d8f37 |
KeyValueAttention | # 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.... | Chenny0808/tatk | KeyValueAttention | false | 13,493 | [
"Apache-2.0"
] | 81 | 1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 | https://github.com/Chenny0808/tatk/tree/1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 |
GMMLoss | import torch
import numpy as np
import torch.nn as nn
class GMMLoss(nn.Module):
def __init__(self):
super(GMMLoss, self).__init__()
def forward(self, x, mu, std, pi):
x = x.unsqueeze(-1)
distrib = torch.exp(-((x - mu) / std) ** 2 / 2) / (std * np.sqrt(2 *
np.pi))
... | 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... | LEEYOONHYUNG/MelNet | GMMLoss | false | 2,476 | [
"MIT"
] | 0 | ea899847658a2e6784f706663d130c56258839de | https://github.com/LEEYOONHYUNG/MelNet/tree/ea899847658a2e6784f706663d130c56258839de |
NB | # 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
assert_size... | SchubertLab/mvTCR | NB | false | 8,750 | [
"MIT"
] | 16 | d815749e24650f69ef68054e0078d490af91b71d | https://github.com/SchubertLab/mvTCR/tree/d815749e24650f69ef68054e0078d490af91b71d |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss()
def forward(self, input, target):
logp = self.ce(input, tar... | 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
... | BaoLocPham/hum2song | FocalLoss | false | 13,371 | [
"MIT"
] | 108 | 706b7fdf838944e2aabe0ae331c0867cb67f6fbc | https://github.com/BaoLocPham/hum2song/tree/706b7fdf838944e2aabe0ae331c0867cb67f6fbc |
TdnnAffine | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | ishine/asv-subtools | TdnnAffine | false | 15,646 | [
"Apache-2.0"
] | 370 | 597dcb29a772b8113dbe7ab64f0d4cc1da298707 | https://github.com/ishine/asv-subtools/tree/597dcb29a772b8113dbe7ab64f0d4cc1da298707 |
Sentence_Maxpool | # 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_... | HS310164/howto100m | Sentence_Maxpool | false | 11,464 | [
"Apache-2.0"
] | 0 | e3952a77c268466de2b9174ae8983c528b91397d | https://github.com/HS310164/howto100m/tree/e3952a77c268466de2b9174ae8983c528b91397d |
SimpleClampModel | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleClampModel | false | 14,640 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
ScalarFilter | # 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... | HKUST-KnowComp/DualMessagePassing | ScalarFilter | false | 8,180 | [
"MIT"
] | 12 | d29d627be2a8c8f24b52e3db2c383e33a059aaa7 | https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7 |
PoseMap | import torch
import torch.nn as nn
class PoseMap(nn.Module):
def __init__(self):
super(PoseMap, self).__init__()
pass
def forward(self, x):
assert len(x.shape) == 4, 'The HeatMap shape should be BxCxHxW'
res = x.sum(dim=1, keepdim=True)
H = x.shape[2]
W = x.sh... | 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... | MrChenFeng/Project-Template | PoseMap | false | 860 | [
"MIT"
] | 0 | 42a335c6abb710bbae6407cbb0ca461533bc12f9 | https://github.com/MrChenFeng/Project-Template/tree/42a335c6abb710bbae6407cbb0ca461533bc12f9 |
SCAttention | # 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.... | YehLi/xmodaler | SCAttention | false | 14,705 | [
"Apache-2.0"
] | 830 | 5340054398c076cfa717317d151ca595c5e37198 | https://github.com/YehLi/xmodaler/tree/5340054398c076cfa717317d151ca595c5e37198 |
DQN_xy3 | # 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 ... | CoAxLab/azad | DQN_xy3 | false | 17,178 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
ResizeTransform | import torch
import torch.nn as nn
import torch.nn.functional as nnf
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.0 / vel_resize
... | 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... | NingAnMe/voxelmorph | ResizeTransform | false | 2,699 | [
"Apache-2.0"
] | 0 | 3a1a4c2f456af2dba5552efc1b08c68af38e54dc | https://github.com/NingAnMe/voxelmorph/tree/3a1a4c2f456af2dba5552efc1b08c68af38e54dc |
EqualConv2d | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | EqualConv2d | false | 11,601 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
TransformerEncoderLayer | import torch
from torch import Tensor
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YiwenShaoStephen/snowfall | TransformerEncoderLayer | false | 14,704 | [
"Apache-2.0"
] | 145 | 949226f35b29c629cb03cae36fa43da5993d27a3 | https://github.com/YiwenShaoStephen/snowfall/tree/949226f35b29c629cb03cae36fa43da5993d27a3 |
eca_block | import math
import torch
import torch.nn as nn
class eca_block(nn.Module):
def __init__(self, channel, b=1, gamma=2):
super(eca_block, self).__init__()
kernel_size = int(abs((math.log(channel, 2) + b) / gamma))
kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | RuidongDavidLin/YOLOV4-tiny-Pytorch | eca_block | false | 2,783 | [
"MIT"
] | 0 | f2bb941ff894e12551bf285eb09fd42db2fb3dee | https://github.com/RuidongDavidLin/YOLOV4-tiny-Pytorch/tree/f2bb941ff894e12551bf285eb09fd42db2fb3dee |
UNetUpsamplingBlock | # 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.... | TropComplique/bicycle-gan | UNetUpsamplingBlock | false | 18,047 | [
"MIT"
] | 4 | 4bc8f4cdbe138e23c8a02c408cfb8e2ff7dfe6ab | https://github.com/TropComplique/bicycle-gan/tree/4bc8f4cdbe138e23c8a02c408cfb8e2ff7dfe6ab |
Chebyshev_GL | from torch.nn import Module
import math
import torch
from torch.nn.modules import Module
from torch.nn.parameter import Parameter
class Chebyshev_GL(Module):
"""
GCN k-hop Layers
x' = Sigma^k-1 (Z^k * w0^k), Z^k= polynomial
"""
def __init__(self, in_features, out_features, k_hop, bias=True):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn.modules import Module
from... | Brain03Yao/M2TGCN | Chebyshev_GL | false | 17,005 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
DilatedResidualLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | LalehSamadfam/tcn-isba | DilatedResidualLayer | false | 2,494 | [
"MIT"
] | 0 | cd2d2c27723e77ba658c695b8b0ba64e4835acf4 | https://github.com/LalehSamadfam/tcn-isba/tree/cd2d2c27723e77ba658c695b8b0ba64e4835acf4 |
MultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | WenjingXia/wenet | MultiHeadedAttention | false | 1,244 | [
"Apache-2.0"
] | 0 | 9a1fd005cd06be16518a5476076b2ae6af2ec41a | https://github.com/WenjingXia/wenet/tree/9a1fd005cd06be16518a5476076b2ae6af2ec41a |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgP... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | PoCInnovation/Koic | Classify | false | 8,660 | [
"MIT"
] | 13 | eca53b53b7242c1e83213ef9408366ca0a346358 | https://github.com/PoCInnovation/Koic/tree/eca53b53b7242c1e83213ef9408366ca0a346358 |
DownsampleA | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat(... | 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... | gianlucagiudice/PyCIL | DownsampleA | false | 3,676 | [
"MIT"
] | 0 | 0db88f239b935ea6d0047918a2a55a703f707b04 | https://github.com/gianlucagiudice/PyCIL/tree/0db88f239b935ea6d0047918a2a55a703f707b04 |
UnfoldTemporalWindows | # 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... | PINTO0309/MS-G3D | UnfoldTemporalWindows | false | 14,137 | [
"MIT"
] | 343 | 5f0f7740ed8543bd0e288affca2a76541c83669e | https://github.com/PINTO0309/MS-G3D/tree/5f0f7740ed8543bd0e288affca2a76541c83669e |
EnsembleFC | import torch
import torch.nn as nn
import torch.utils.data
class EnsembleFC(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Weiyuhong-1998/DI-engine | EnsembleFC | false | 14,574 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
Res | # 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
import t... | w-cheng/pytorch-struct | Res | false | 13,071 | [
"MIT"
] | 0 | e51fecc1473925e4c44de135c4a3240fcb20fa40 | https://github.com/w-cheng/pytorch-struct/tree/e51fecc1473925e4c44de135c4a3240fcb20fa40 |
BinaryDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | MargeryLab/nnConRes | BinaryDiceLoss | false | 9,324 | [
"Apache-2.0"
] | 0 | a5aba912d0f0f30490ae820fb6d3dbb8cf1556d4 | https://github.com/MargeryLab/nnConRes/tree/a5aba912d0f0f30490ae820fb6d3dbb8cf1556d4 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AWarno/CodeHateChallenge | LayerNorm | false | 18,443 | [
"MIT"
] | 3 | f02bab7ca93a2441b7b2901094bedee72830b266 | https://github.com/AWarno/CodeHateChallenge/tree/f02bab7ca93a2441b7b2901094bedee72830b266 |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Prince326/PySyft | AvgPool2d | false | 9,357 | [
"Apache-2.0"
] | 0 | c7167680e9020853c353a2a725ff79f3df2bef05 | https://github.com/Prince326/PySyft/tree/c7167680e9020853c353a2a725ff79f3df2bef05 |
PositionalEmbedding | import math
import torch
class PositionalEmbedding(torch.nn.Module):
def __init__(self):
super(PositionalEmbedding, self).__init__()
def forward(self, inputs):
if inputs.dim() != 3:
raise ValueError('The rank of input must be 3.')
length = inputs.shape[1]
channels... | 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... | THUNLP-MT/PLM4MT | PositionalEmbedding | false | 1,103 | [
"BSD-3-Clause"
] | 0 | 85bd2ee9d96b07ac827e14d4b3e5b0d0924c3401 | https://github.com/THUNLP-MT/PLM4MT/tree/85bd2ee9d96b07ac827e14d4b3e5b0d0924c3401 |
Gated_Recurrent_Unit | # 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.utils.data
from ... | SpartaG117/scene_graph_benchmark | Gated_Recurrent_Unit | false | 1,162 | [
"MIT"
] | 0 | e2e49940dd2f752b1faf9ae26707435ba3441bcb | https://github.com/SpartaG117/scene_graph_benchmark/tree/e2e49940dd2f752b1faf9ae26707435ba3441bcb |
tLNv2 | import torch
import torch.nn as nn
from torch.autograd import Variable
def my_mean(x):
f = x.shape[-1]
mean = x[..., 0]
for i in range(1, f):
mean += x[..., i]
return mean[..., None] / f
class tLNv2(nn.Module):
def __init__(self, dimension, eps=1e-08, trainable=True):
super(tLNv... | 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
from torch.autograd import Variable
assert_size_stride = ... | rbodo/pytorch-OpCounter | tLNv2 | false | 7,546 | [
"MIT"
] | 1 | 1857cbb5f9e53343fb349af84efdfde2554a2691 | https://github.com/rbodo/pytorch-OpCounter/tree/1857cbb5f9e53343fb349af84efdfde2554a2691 |
Bilinear | # 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 Tensor
from torch import nn as nn
import torch.nn.functional a... | gdevos010/darts | Bilinear | false | 3,667 | [
"Apache-2.0"
] | 0 | 96c97c1e241500ae7b91d32bbfa21d811e4a7d71 | https://github.com/gdevos010/darts/tree/96c97c1e241500ae7b91d32bbfa21d811e4a7d71 |
VitMlpHead | import torch
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True, help=
'Path to input JSON')
group.add_argument('--json-keys', nargs='+', default=['text'], help=
'space separate ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | ExaSearch/Megatron-DeepSpeed | VitMlpHead | false | 13,663 | [
"MIT"
] | 71 | 215dcf9fd4d18d9efa1d15d06c3eb85572957bf3 | https://github.com/ExaSearch/Megatron-DeepSpeed/tree/215dcf9fd4d18d9efa1d15d06c3eb85572957bf3 |
ClusterLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.ut... | LakeAndCat/CluOReg | ClusterLayer | false | 758 | [
"MIT"
] | 0 | ba50cb056061b3833050d32e532e08152bdc8de2 | https://github.com/LakeAndCat/CluOReg/tree/ba50cb056061b3833050d32e532e08152bdc8de2 |
GumbelSoftmax | # 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.... | Kaya176/GMVAE | GumbelSoftmax | false | 9,252 | [
"MIT"
] | 0 | 6369be52dbac796e2f836f51b16aaa5c61247350 | https://github.com/Kaya176/GMVAE/tree/6369be52dbac796e2f836f51b16aaa5c61247350 |
RegLoss | import torch
import torch.nn as nn
class RegLoss(nn.Module):
""" RegLoss, L2 regularization on model parameters
"""
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, parameters):
reg_loss = None
for W in parameters:
if reg_loss is None:
... | 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_... | MIracleyin/RecBole-notebook | RegLoss | false | 9,563 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
Convolutional | import torch
import torch.nn.functional as F
import torch.nn as nn
class Convolutional(nn.Module):
def __init__(self, num_classes=10):
super().__init__()
self.conv1 = nn.Conv2d(1, 16, 5)
self.conv2 = nn.Conv2d(16, 32, 5)
self.fc1 = nn.Linear(512, 128)
self.fc2 = nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | f4str/digit-recognizer | Convolutional | false | 3,488 | [
"MIT"
] | 0 | 67c175c683b22a3bf9d8a28dce812a82e08039d5 | https://github.com/f4str/digit-recognizer/tree/67c175c683b22a3bf9d8a28dce812a82e08039d5 |
ResizeCat | import torch
import torch.nn as nn
class ResizeCat(nn.Module):
def __init__(self, **kwargs):
super(ResizeCat, self).__init__()
def forward(self, at1, at3, at5):
_N, _C, H, W = at1.size()
resized_at3 = nn.functional.interpolate(at3, (H, W))
resized_at5 = nn.functional.interpol... | 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... | GunjanChourasia/WS_DAN_PyTorch | ResizeCat | false | 2,316 | [
"MIT"
] | 0 | 6c12a1b5b0b8980e3b69d44474e0b5edb455570c | https://github.com/GunjanChourasia/WS_DAN_PyTorch/tree/6c12a1b5b0b8980e3b69d44474e0b5edb455570c |
PKTCosSim | # 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.triton_helpers import libdevice, math as tl_math
im... | wangxianliang/FaceX-Zoo | PKTCosSim | false | 13,095 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
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.... | wukevin/RoseTTAFold | EncoderLayer | false | 4,573 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
Mapping | # 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 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... | tasfia/BMCoGAN | Mapping | false | 13,108 | [
"MIT"
] | 0 | 0d400c2c71dbfb69af422afc487f65afb98de8af | https://github.com/tasfia/BMCoGAN/tree/0d400c2c71dbfb69af422afc487f65afb98de8af |
ReconstructionLoss | import torch
from functools import reduce
import torch.nn as nn
class BaseModule(nn.Module):
"""
Implements the basic module.
All other modules inherit from this one
"""
def load_w(self, checkpoint_path):
"""
Loads a checkpoint into the state_dict.
:param checkpoint_path:... | 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 functools import reduce
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019 | ReconstructionLoss | false | 8,607 | [
"MIT"
] | 12 | b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 | https://github.com/NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019/tree/b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 |
PixelNormLayer | # 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_... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | PixelNormLayer | false | 7,884 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
ToRGB | # 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.autograd import Function
import math
from torch import nn
import torc... | ArashVahabpour/encoder4editing-contrastive | ToRGB | false | 13,322 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
Embedding | # 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 numpy as np
import torch as t
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_... | arjunsesh/lrr-neurips | Embedding | false | 6,232 | [
"MIT"
] | 1 | d78106daec1e729b02a0452f74a37bf004ed243c | https://github.com/arjunsesh/lrr-neurips/tree/d78106daec1e729b02a0452f74a37bf004ed243c |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, state_dim, action_dim):
super(Discriminator, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, sta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | nikhilbarhate99/Deterministic-GAIL-PyTorch | Discriminator | false | 16,193 | [
"MIT"
] | 64 | 36843739dd7b0ca58e9fcaf923cc6735a5d7ffef | https://github.com/nikhilbarhate99/Deterministic-GAIL-PyTorch/tree/36843739dd7b0ca58e9fcaf923cc6735a5d7ffef |
DenoisingDownsample | # 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... | arkel23/mmgeneration | DenoisingDownsample | false | 9,944 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
PhonyLanguageModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class PhonyLanguageModel(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
lm_x = x.clone().detach().float() * 0
return F.log_softmax(lm_x, 1)
def get_inputs():
return [torch.rand([4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | daemon/vivi | PhonyLanguageModel | false | 9,942 | [
"MIT"
] | 0 | 6b7819006c944a756bf8a7b6d8beed92d19eb51a | https://github.com/daemon/vivi/tree/6b7819006c944a756bf8a7b6d8beed92d19eb51a |
SpatialAttention2d | import torch
import torch.nn as nn
import torch._utils
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.... | elmajdma/seismic-deeplearning | SpatialAttention2d | false | 15,298 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc |
TorchAdd | import torch
import torch.nn as nn
class TorchAdd(nn.Module):
"""TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | lawwu/nni | TorchAdd | false | 10,595 | [
"MIT"
] | 0 | b869dd48dfe36392e7b78c70ea35eb6d4b4779dc | https://github.com/lawwu/nni/tree/b869dd48dfe36392e7b78c70ea35eb6d4b4779dc |
_FPNUp | # 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 ... | simonmeister/pytorch-mono-depth | _FPNUp | false | 16,456 | [
"MIT"
] | 56 | 713c70e2fdae6d9d6e0322febadfedcaee9470d3 | https://github.com/simonmeister/pytorch-mono-depth/tree/713c70e2fdae6d9d6e0322febadfedcaee9470d3 |
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