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 |
|---|---|---|---|---|---|---|---|---|---|---|
TLU | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class TLU(nn.Module):
def __init__(self, num_features):
super(TLU, self).__init__()
self.num_features = num_features
self.tau = Parameter(torch.Tensor(1, num_features, 1, 1),
requires_grad=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch... | COATZ/ShapeConv | TLU | false | 13,440 | [
"Apache-2.0"
] | 57 | f34f4e95ee2b69ac645fd5ba608e3c11cfadfded | https://github.com/COATZ/ShapeConv/tree/f34f4e95ee2b69ac645fd5ba608e3c11cfadfded |
LinearProximal | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | belbahrim/twin-causal-net | LinearProximal | false | 3,203 | [
"MIT"
] | 0 | f45d5a61ed9039ae7d0cd615d95212f11a5a2086 | https://github.com/belbahrim/twin-causal-net/tree/f45d5a61ed9039ae7d0cd615d95212f11a5a2086 |
LinearBottleneckLayer | import torch
import torch.nn as nn
class LinearBottleneckLayer(nn.Module):
""" Bottleneck Layer """
def __init__(self, d_features, d_hid, d_out=None, dropout=0.1):
super().__init__()
if d_out is None:
d_out = d_features
self.encode = nn.Linear(d_features, d_hid)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jincheng-Sun/Kylearn-pytorch | LinearBottleneckLayer | false | 660 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | # 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.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 11,829 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=3, multi_branch=False):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
self.conv1 = nn.Conv2d(2, 1, 3, padding=1, bias=False)
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_... | SamitHuang/CELNet | SpatialAttention | false | 5,798 | [
"MIT"
] | 1 | 51e067fdb16e723a45a0a60399d568b58cdc011d | https://github.com/SamitHuang/CELNet/tree/51e067fdb16e723a45a0a60399d568b58cdc011d |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | AndreHe02/glow-tts | LayerNorm | false | 1,948 | [
"MIT"
] | 0 | 683f68f17790f2f46c23e9d3eadbcac352d82e2b | https://github.com/AndreHe02/glow-tts/tree/683f68f17790f2f46c23e9d3eadbcac352d82e2b |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AbeJLazaro/TraductorEspanolOtomi | Generator | false | 1,911 | [
"MIT"
] | 0 | 75e1558d3b1a7efe9beb3c7d992c3bf1d3d88d0b | https://github.com/AbeJLazaro/TraductorEspanolOtomi/tree/75e1558d3b1a7efe9beb3c7d992c3bf1d3d88d0b |
Conv2dWS | import torch
import torch.nn.functional as F
from torch import nn
class Conv2dWS(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2dWS, self).__init__(in_channels, out_channels,
kernel_size, stride,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | Conv2dWS | false | 15,083 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
_TestNetStrided | import torch
import torch.nn.functional as F
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class _TestNetStrided(torch.nn.Module):
def __init__(self):
super(_TestNetStrided, self).__init__()
self.conv1 = torch.nn.Conv2d(1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rohan-Chaudhury/aimet | _TestNetStrided | false | 17,959 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
ActNorm | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
class ActNorm(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Parameter(torch.Tensor... | 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
from torch.nn import Parame... | XinZhang525/fGAIL | ActNorm | false | 18,128 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b |
idct_8x8 | # 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 itertools
import numpy as np
import torch.nn as nn
assert_size_stride = t... | mlomnitz/DifferentiableJPEG | idct_8x8 | false | 16,112 | [
"MIT"
] | 86 | a5767feba955a1bcb78600135a09c36a806f6249 | https://github.com/mlomnitz/DifferentiableJPEG/tree/a5767feba955a1bcb78600135a09c36a806f6249 |
OZELoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class OZELoss(nn.Module):
"""Custom loss for TRNSys metamodel.
Compute, for temperature and consumptions, the intergral of the squared differences
over time. Sum the log with a coeficient ``alpha``.
.. math::
... | 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.utils.data
import torch.nn as nn
import torch.nn.parallel
as... | YanLu-nyu/transferlearning | OZELoss | false | 14,627 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
DenseResidualBlock | import torch
import torch.nn as nn
class DenseResidualBlock(nn.Module):
"""
Wrapping a number of residual layers for residual block. Will be used as building block in FiLM hyper-networks.
:param in_size: (int) Number of features for input representation.
:param out_size: (int) Number of features for o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DaikiSannoXC/simple-cnaps | DenseResidualBlock | false | 13,546 | [
"MIT"
] | 62 | be35c4522b180eaae8278633b1c6ca7e5bb56ebb | https://github.com/DaikiSannoXC/simple-cnaps/tree/be35c4522b180eaae8278633b1c6ca7e5bb56ebb |
MessageNormalizer | import torch
import torch.nn as nn
class MessageNormalizer(nn.Module):
def __init__(self, in_features, init_mean=1.0, init_stddev=0.01):
super(MessageNormalizer, self).__init__()
self.in_features = in_features
self.out_features = in_features
self.weight = torch.nn.Parameter(torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ShinyaFUKUMOTO/LeMPA | MessageNormalizer | false | 1,061 | [
"BSD-2-Clause"
] | 0 | 23b8c9f60fc13cf28d4485757d2ae0b3465b3e92 | https://github.com/ShinyaFUKUMOTO/LeMPA/tree/23b8c9f60fc13cf28d4485757d2ae0b3465b3e92 |
SoftGeneratorAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class SoftGeneratorAttention(nn.Module):
def __init__(self):
nn.Module.__init__(self)
def apply_bn(self, x):
bn_module = nn.BatchNorm1d(x.size()[1])
return bn_module(x)
def forward(self, key, x):
attn = t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LinChen-65/pygcn | SoftGeneratorAttention | false | 2,519 | [
"MIT"
] | 0 | 0a77f56fd6d5cb3edc7affc2ba3455733d7da6eb | https://github.com/LinChen-65/pygcn/tree/0a77f56fd6d5cb3edc7affc2ba3455733d7da6eb |
OpenPoseLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class OpenPoseLoss(nn.Module):
def __init__(self):
super(OpenPoseLoss, self).__init__()
def forward(self, saved_for_loss, heatmap_target, heat_mask, paf_target,
paf_mask):
"""
tính loss
Parameters
... | 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... | makotovnjp/Talent5OpenPose | OpenPoseLoss | false | 3,973 | [
"Apache-2.0"
] | 0 | 1ebbbd4f226b6839d7d1627d6c33edd416c137fc | https://github.com/makotovnjp/Talent5OpenPose/tree/1ebbbd4f226b6839d7d1627d6c33edd416c137fc |
MinibatchStdLayer | # 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_... | DannyDannyDanny/DeepPrivacy | MinibatchStdLayer | false | 2,123 | [
"MIT"
] | 0 | 749e260bdcc28a0c12d526f24e4f5315d1b447ad | https://github.com/DannyDannyDanny/DeepPrivacy/tree/749e260bdcc28a0c12d526f24e4f5315d1b447ad |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Worm4047/TVR | BertAttention | false | 14,596 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
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.triton_helpers import libdevice, math as tl_math
fr... | heyitsmine/FewRel | Attention | false | 10,269 | [
"MIT"
] | 0 | 2a2b8ae471298d9eb3557796a085c23b21982fb2 | https://github.com/heyitsmine/FewRel/tree/2a2b8ae471298d9eb3557796a085c23b21982fb2 |
VQVAEQuantize | # 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... | crizCraig/deep-vector-quantization | VQVAEQuantize | false | 15,103 | [
"MIT"
] | 326 | c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 | https://github.com/crizCraig/deep-vector-quantization/tree/c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, d_hid, eps=1e-06):
super(LayerNormalization, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_hid))
self.beta = nn.Parameter(torch.zeros(d_hid))
self.eps = eps
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | alisure-fork/CONTA | LayerNormalization | false | 1,415 | [
"MIT"
] | 0 | dde3e5083f45598d859dde889de3ae85c7a416e9 | https://github.com/alisure-fork/CONTA/tree/dde3e5083f45598d859dde889de3ae85c7a416e9 |
LinearExcitability | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn.parameter import Parameter
assert... | JosephKJ/continual-learning | LinearExcitability | false | 9,165 | [
"MIT"
] | 0 | 2e526cc58ab35d76cddc1df46ee421baea89a727 | https://github.com/JosephKJ/continual-learning/tree/2e526cc58ab35d76cddc1df46ee421baea89a727 |
MINCNet | import torch
import torch.utils.data
from torch.utils import data as data
import torch.nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
class MINCNet(nn.Module):
def __init__(self):
super(MINCNet, self).__init__()
self.ReL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | achrefjarray/ESRGANplus-master | MINCNet | false | 1,403 | [
"Apache-2.0"
] | 0 | ba470ec5c565a6dc8b48575b1e185ef6b796aec6 | https://github.com/achrefjarray/ESRGANplus-master/tree/ba470ec5c565a6dc8b48575b1e185ef6b796aec6 |
ScaledDotProductAttention | import torch
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->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.... | Hcnaeg/DI-engine | ScaledDotProductAttention | false | 2,392 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Halo1236/Dive-into-DL-PyTorch | GlobalAvgPool2d | false | 532 | [
"Apache-2.0"
] | 0 | 586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 | https://github.com/Halo1236/Dive-into-DL-PyTorch/tree/586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 |
Sparsemax | # 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 import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.multiprocessing
import torch.nn as nn
assert_size_s... | ai4ce/DiscoNet | Sparsemax | false | 14,763 | [
"MIT"
] | 80 | 44b57faac3c5be289d33cbbab12b300e3ac767b0 | https://github.com/ai4ce/DiscoNet/tree/44b57faac3c5be289d33cbbab12b300e3ac767b0 |
Fp32LayerNorm | # 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
import torch.utils.data
import torch.onnx.operators
impor... | AbhilashMathews/adahessian | Fp32LayerNorm | false | 4,819 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 |
GroupNormAct | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def swish(x, inplace: 'bool'=False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul... | 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... | dumpmemory/NonDeepNetworks | GroupNormAct | false | 15,262 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.data
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root)."""
super(BertLayerNorm,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Stephen0808/WebQA | BertAttention | false | 11,941 | [
"Apache-2.0"
] | 0 | b9758932a9d0d75167ec837bb6ee8bc571c64681 | https://github.com/Stephen0808/WebQA/tree/b9758932a9d0d75167ec837bb6ee8bc571c64681 |
LRN | # 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_... | melster1010/VIAME | LRN | false | 10,469 | [
"BSD-3-Clause"
] | 0 | 0062265088aae65effbfcd130bfb874c343c785f | https://github.com/melster1010/VIAME/tree/0062265088aae65effbfcd130bfb874c343c785f |
VGG16 | # 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_... | Jiannan-Liu/nCoVSegNet | VGG16 | false | 17,611 | [
"MIT"
] | 5 | 7543e68edff011a7f7b694c97cf0f185d441fd6b | https://github.com/Jiannan-Liu/nCoVSegNet/tree/7543e68edff011a7f7b694c97cf0f185d441fd6b |
MaxPoolBlock | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | 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... | DevilMayNotCry/My_curl | MaxPoolBlock | false | 9,122 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | NceBoy/mmocr | DiceLoss | false | 11,744 | [
"Apache-2.0"
] | 0 | 3fb7a18d7eb44799e75c1991e5da2044b458d411 | https://github.com/NceBoy/mmocr/tree/3fb7a18d7eb44799e75c1991e5da2044b458d411 |
BoxOffsetIntersection | # 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_... | google-research/smore | BoxOffsetIntersection | false | 15,460 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
CosineActivation | # 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
from torch im... | jaredfeng-ca/Time2Vec-PyTorch | CosineActivation | false | 3,704 | [
"MIT"
] | 0 | b42205f6721f5a6faf16134e604af28476490d0a | https://github.com/jaredfeng-ca/Time2Vec-PyTorch/tree/b42205f6721f5a6faf16134e604af28476490d0a |
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.... | DMIRLAB-Group/Dassl.pytorch | Prototypes | false | 5,035 | [
"MIT"
] | 1 | 79052448cc0b0622f14e9768dbd6e6c0598fe6d1 | https://github.com/DMIRLAB-Group/Dassl.pytorch/tree/79052448cc0b0622f14e9768dbd6e6c0598fe6d1 |
FFN | import torch
import torch.nn as nn
import torch as t
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of 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 import triton_helpers
from torch._inductor.runtime.... | yhgon/Transformer-TTS | FFN | false | 13,138 | [
"MIT"
] | 0 | 5f34945cb5500d484275700c4e393ed125d5e753 | https://github.com/yhgon/Transformer-TTS/tree/5f34945cb5500d484275700c4e393ed125d5e753 |
Net1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ringier-data/deep-learning-containers | Net1 | false | 7,547 | [
"Apache-2.0"
] | 1 | e939ceee48a426f9ae4e0b50317dc2fa8845a312 | https://github.com/ringier-data/deep-learning-containers/tree/e939ceee48a426f9ae4e0b50317dc2fa8845a312 |
LogisticLoss | import torch
class LogisticLoss(torch.nn.Module):
def __init__(self):
super(LogisticLoss, self).__init__()
def forward(self, logits, targets, multi_label=False):
y = targets.float()
n_plus = torch.sum(y, dim=0)
n_minus = torch.sum(1.0 - y, dim=0)
n_plus_rate = (n_plus... | 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... | brainsqueeze/Kaggle-competitions | LogisticLoss | false | 3,244 | [
"MIT"
] | 0 | e734ca71303619fd2c9a6f10aaf98b2c0a800758 | https://github.com/brainsqueeze/Kaggle-competitions/tree/e734ca71303619fd2c9a6f10aaf98b2c0a800758 |
CharbonnierLoss | import functools
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... | 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 functools
from torch ... | Lotayou/BasicSR | CharbonnierLoss | false | 2,576 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ahren09/FinerFact | BertSelfAttention | false | 18,052 | [
"MIT"
] | 9 | 68df3799fbfadd56fa69b019ca6fba0c482f21d3 | https://github.com/Ahren09/FinerFact/tree/68df3799fbfadd56fa69b019ca6fba0c482f21d3 |
RepulsionLoss | # 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
... | RRemixx/DMRDenoise | RepulsionLoss | false | 14,267 | [
"MIT"
] | 79 | 026d25f9eaf98fdfd85a67caeb9b49cab71148e9 | https://github.com/RRemixx/DMRDenoise/tree/026d25f9eaf98fdfd85a67caeb9b49cab71148e9 |
AverageAttention | # 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.cuda
import torch.distributed
assert_size_str... | Zer0-dev115/OpenNMT-py | AverageAttention | false | 12,084 | [
"MIT"
] | 0 | 028c76b34779223ee6b3eb224b99617552987100 | https://github.com/Zer0-dev115/OpenNMT-py/tree/028c76b34779223ee6b3eb224b99617552987100 |
GEGLU | # 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
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._... | ClashLuke/gpt-neox | GEGLU | false | 428 | [
"MIT"
] | 0 | 3291d0e6c867d9d328b96e8377f5b77c6f66c323 | https://github.com/ClashLuke/gpt-neox/tree/3291d0e6c867d9d328b96e8377f5b77c6f66c323 |
Actor | import torch
from torch import nn
import torch.nn.functional as F
class Actor(nn.Module):
"""Actor model
Parameters:
args (object): Parameter class
"""
def __init__(self, state_dim, action_dim, wwid):
super(Actor, self).__init__()
self.wwid = torch.Tensor([wwid])
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | zhan0903/cerl | Actor | false | 4,657 | [
"Apache-2.0"
] | 0 | 6fb8aca9cb78b72947237edf2b9ed8362bd43829 | https://github.com/zhan0903/cerl/tree/6fb8aca9cb78b72947237edf2b9ed8362bd43829 |
Net | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
class Net(nn.Module):
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | huangzsdy/pytorch_basic_learning | Net | false | 3,686 | [
"Apache-2.0"
] | 0 | 7880bc3fcee1d38623d93fa2a36482ccde0e335a | https://github.com/huangzsdy/pytorch_basic_learning/tree/7880bc3fcee1d38623d93fa2a36482ccde0e335a |
MultiheadAttention | import torch
from torch import nn
import torch.utils
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | muqiaoy/dl_signal | MultiheadAttention | false | 16,135 | [
"MIT"
] | 54 | 3a30d14982016644bfc96a7d1ca0109b441f17fd | https://github.com/muqiaoy/dl_signal/tree/3a30d14982016644bfc96a7d1ca0109b441f17fd |
GAT | # 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.... | caojiangxia/BiGI | GAT | false | 15,007 | [
"MIT"
] | 57 | ed54c20523a5b3f295b90a9c08f7c54e8258d04a | https://github.com/caojiangxia/BiGI/tree/ed54c20523a5b3f295b90a9c08f7c54e8258d04a |
PointWiseFeedForward | import torch
class PointWiseFeedForward(torch.nn.Module):
def __init__(self, hidden_units, dropout_rate):
super(PointWiseFeedForward, self).__init__()
self.conv1 = torch.nn.Conv1d(hidden_units, hidden_units, kernel_size=1)
self.dropout1 = torch.nn.Dropout(p=dropout_rate)
self.relu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | RenqinSS/RecSys | PointWiseFeedForward | false | 11,812 | [
"MIT"
] | 0 | 932d54e47af79576b67e28364ae6e97d99ededf4 | https://github.com/RenqinSS/RecSys/tree/932d54e47af79576b67e28364ae6e97d99ededf4 |
MedianPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | vztu/DebandingNet | MedianPool2d | false | 4,517 | [
"MIT"
] | 0 | 4af8e83ffbfc70dc220dd6fea2827fb75796f10c | https://github.com/vztu/DebandingNet/tree/4af8e83ffbfc70dc220dd6fea2827fb75796f10c |
SoftTargetCrossEntropy | # 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
... | javierrodenas/clearml_javi | SoftTargetCrossEntropy | false | 10,360 | [
"Apache-2.0"
] | 0 | b6326104fe6a6f522223c2ac3d87468990a9e6f2 | https://github.com/javierrodenas/clearml_javi/tree/b6326104fe6a6f522223c2ac3d87468990a9e6f2 |
Discriminator_D1 | # 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... | e96031413/tfvaegan | Discriminator_D1 | false | 10,110 | [
"MIT"
] | 0 | 4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 | https://github.com/e96031413/tfvaegan/tree/4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 |
NeuralNetwork | import torch
import torch.nn as nn
class NeuralNetwork(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim=None):
""" Simple two-layer neural network.
"""
super(NeuralNetwork, self).__init__()
if hidden_dim is None:
hidden_dim = in_dim * 2
self.l1 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | adewynter/Lightboard | NeuralNetwork | false | 12,059 | [
"Apache-2.0"
] | 0 | f02eae64f11a989030b52314aa66709477274eb3 | https://github.com/adewynter/Lightboard/tree/f02eae64f11a989030b52314aa66709477274eb3 |
BatchNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch import tensor
import torch.nn as... | dlvp/madminer | BatchNorm | false | 1,848 | [
"MIT"
] | 0 | 4ae7d9b73452848a6c9d1b81b50ef316ff7a054f | https://github.com/dlvp/madminer/tree/4ae7d9b73452848a6c9d1b81b50ef316ff7a054f |
_TextureConvGroup | import torch
from torch import nn
from torch.nn import functional as F
def reflect_padding(x, f, s, half=False):
if half:
denom = 2
else:
denom = 1
_, _, h, w = x.shape
pad_w = w * (s / denom - 1) + f - s
pad_h = h * (s / denom - 1) + f - s
if pad_w % 2 == 1:
pad_l = 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, math as tl_math
fr... | RicCu/NeuralStyle | _TextureConvGroup | false | 5,777 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
Model | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor'):
return x * y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Archermmt/tvm | Model | false | 11,192 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
TransferNet | # 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.... | ThomasRanvier/cnn_style_transfer | TransferNet | false | 1,222 | [
"MIT"
] | 0 | 90b6c76c20263c22f4e45184d572284726ecbd7b | https://github.com/ThomasRanvier/cnn_style_transfer/tree/90b6c76c20263c22f4e45184d572284726ecbd7b |
SimpleShortCut | # 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... | RaoefTaki/MNTDP-forked | SimpleShortCut | false | 8,680 | [
"MIT"
] | 15 | d9ea59a6638f6cdc93eca180ab02672f5bf5d2a1 | https://github.com/RaoefTaki/MNTDP-forked/tree/d9ea59a6638f6cdc93eca180ab02672f5bf5d2a1 |
SimpleLinearModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleLinearModule | false | 3,391 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Crop | # 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.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | pystiche/papers | Crop | false | 7,496 | [
"BSD-3-Clause"
] | 1 | 0d8179dc51f6eda0b27fa525dc0b86b866bc88e1 | https://github.com/pystiche/papers/tree/0d8179dc51f6eda0b27fa525dc0b86b866bc88e1 |
TripletLoss | # 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.nn.modules.distan... | tbmoon/facenet | TripletLoss | false | 16,536 | [
"MIT"
] | 231 | b3aec1a930f22a5a9597efa7072373c0ff93663f | https://github.com/tbmoon/facenet/tree/b3aec1a930f22a5a9597efa7072373c0ff93663f |
UnfoldTemporalWindows | import torch
import torch.nn as nn
class UnfoldTemporalWindows(nn.Module):
def __init__(self, window_size, window_stride, window_dilation=1):
super().__init__()
self.window_size = window_size
self.window_stride = window_stride
self.window_dilation = window_dilation
self.pa... | 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... | Rgtemze/PersonalityRecognition | UnfoldTemporalWindows | false | 5,758 | [
"MIT"
] | 1 | 90ddd9c02e595d685b8c395ae94d50090288d1f0 | https://github.com/Rgtemze/PersonalityRecognition/tree/90ddd9c02e595d685b8c395ae94d50090288d1f0 |
Spatial_Attention_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.... | msalvato/pytorch_geometric_temporal | Spatial_Attention_layer | false | 10,594 | [
"MIT"
] | 0 | 149bd46d3b2bddfc3570e31a91a3f53e8873d50e | https://github.com/msalvato/pytorch_geometric_temporal/tree/149bd46d3b2bddfc3570e31a91a3f53e8873d50e |
Conv2d_GN_ReLUx2 | import torch
import torch.nn as nn
class Conv2d_GN_ReLU(nn.Module):
""" Implements a module that performs
conv2d + groupnorm + ReLU +
Assumes kernel size is odd
"""
def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
):
super(Conv2d_GN_ReLU, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_ReLUx2 | false | 2,246 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
Skew | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Ismail-Mustapha/tutorials | Skew | false | 13,851 | [
"BSD-3-Clause"
] | 6,424 | 0ccfbf0047db855e93e2aadb43c89c92e89f52b8 | https://github.com/Ismail-Mustapha/tutorials/tree/0ccfbf0047db855e93e2aadb43c89c92e89f52b8 |
MultiHeadAttention | import torch
import numpy as np
import torch.nn as nn
def dot_product_attention(queries, keys, values, normalise=True):
"""
:param queries:[batch_size, N_target, key_size]
:param keys:[batch_size, N_context, key_size]
:param values: []
:param normalise:
:return:
"""
key_size = keys.sha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JuliusSchwartz/FlowMO | MultiHeadAttention | false | 13,967 | [
"MIT"
] | 53 | e221d989914f906501e1ad19cd3629d88eac1785 | https://github.com/JuliusSchwartz/FlowMO/tree/e221d989914f906501e1ad19cd3629d88eac1785 |
FastSigmoid | import torch
import torch.utils.data
import torch
import torch.nn as nn
class FastSigmoid(nn.Module):
def __init__(self):
super(FastSigmoid, self).__init__()
def forward(self, x):
abs = torch.abs(x) + 1
return torch.div(x, abs)
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch
import torch.nn as nn
assert_size_st... | zhuxyme/zxySRFBN_CVPR2019 | FastSigmoid | false | 13,172 | [
"MIT"
] | 0 | c1afe776e7759bc05f2235b6db708e337cf2ae0e | https://github.com/zhuxyme/zxySRFBN_CVPR2019/tree/c1afe776e7759bc05f2235b6db708e337cf2ae0e |
ncm_output | # 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_... | raphael-baena/clean-train | ncm_output | false | 12,933 | [
"MIT"
] | 0 | f65fcecc11203b12f27d14964944db6941b513cc | https://github.com/raphael-baena/clean-train/tree/f65fcecc11203b12f27d14964944db6941b513cc |
GlobalAvgPool2d | # 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... | Exdenta/torchsat | GlobalAvgPool2d | false | 13,647 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
Triaffine | import torch
import torch.nn as nn
class Triaffine(nn.Module):
"""
Triaffine layer for second-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y, z)` is computed as :math:`x^T z^T W y`.
Usually, :... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ashim95/parser | Triaffine | false | 6,255 | [
"MIT"
] | 1 | 61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 | https://github.com/ashim95/parser/tree/61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 |
BilinearRanking | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
class BilinearRanking(nn.Module):
def __init__(self, n_classes: 'int'=2, emb_size: 'int'=768, block_size:
'int'=8):
super().__init__()
self.n_classes = n_classes
self.emb_size = emb_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IgnatovFedor/DeepPavlov | BilinearRanking | false | 9,177 | [
"Apache-2.0"
] | 0 | 02ba9c4b2919384c142c170c7f89c65cf05dd426 | https://github.com/IgnatovFedor/DeepPavlov/tree/02ba9c4b2919384c142c170c7f89c65cf05dd426 |
LSID | # 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 math
import torch.nn a... | cydonia999/Learning_to_See_in_the_Dark_PyTorch | LSID | false | 15,162 | [
"MIT"
] | 77 | 470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 | https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch/tree/470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 |
ClassicalConv6 | # 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.... | mit-han-lab/pytorch-quantum | ClassicalConv6 | false | 16,114 | [
"MIT"
] | 98 | 05cf000d689307f6b1fe02d12744ad455685935b | https://github.com/mit-han-lab/pytorch-quantum/tree/05cf000d689307f6b1fe02d12744ad455685935b |
FloorDivAssign | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
d... | PogChamper/torch2trt | FloorDivAssign | false | 14,186 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
HardSwish | import torch
from torch import nn
import torch.nn.functional as F
def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False) ->torch.Tensor:
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
"""
HardSwish activiation layer.
Applies th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynam... | L-Net-1992/towhee | HardSwish | false | 13,977 | [
"Apache-2.0"
] | 365 | 471de97bf9c5443efaf3b62fd440b3ebdb6d5903 | https://github.com/L-Net-1992/towhee/tree/471de97bf9c5443efaf3b62fd440b3ebdb6d5903 |
Tanh | import torch
import torch.utils.data
import torch.nn as nn
import torch._utils
from torch import optim as optim
import torch.nn.parallel
class Tanh(nn.Module):
def __init__(self, inplace=False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.tanh_(... | 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.utils.data
import torch.nn as nn
import torch._utils
from torch im... | lovelinability/pytorch_image_models | Tanh | false | 7,146 | [
"Apache-2.0"
] | 1 | 7c54200f3de7611ab1222a37088eb7f66ae2858f | https://github.com/lovelinability/pytorch_image_models/tree/7c54200f3de7611ab1222a37088eb7f66ae2858f |
WassersteinLoss | import torch
def torch_cdf_loss(tensor_a, tensor_b, p=1):
tensor_a = tensor_a / (torch.sum(tensor_a, dim=-1, keepdim=True) + 1e-14)
tensor_b = tensor_b / (torch.sum(tensor_b, dim=-1, keepdim=True) + 1e-14)
cdf_tensor_a = torch.cumsum(tensor_a, dim=-1)
cdf_tensor_b = torch.cumsum(tensor_b, dim=-1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | nikitadhawan/SimCLR | WassersteinLoss | false | 7,347 | [
"MIT"
] | 1 | 7d87b384b1edb68e7ba86601b26f76e6da214718 | https://github.com/nikitadhawan/SimCLR/tree/7d87b384b1edb68e7ba86601b26f76e6da214718 |
ResnetBlockInplaceNormShallowConv1d | import torch
import torch.nn as nn
class ResnetBlockInplaceNormShallowConv1d(nn.Module):
""" Fully connected ResNet Block imeplemented with group convolutions and weight/spectral normalizations.
Args:
size_in (int): input dimension
groups (int): number of groups for group convolutions
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | taconite/MetaAvatar-release | ResnetBlockInplaceNormShallowConv1d | false | 16,525 | [
"MIT"
] | 60 | c9403a478ee82232633d25f65f108befd21d04e9 | https://github.com/taconite/MetaAvatar-release/tree/c9403a478ee82232633d25f65f108befd21d04e9 |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | wwjfsfs/wwjyyds | ConvBlock | false | 13,124 | [
"MIT"
] | 0 | 80cd6267fde7cd98838078a0d5178a557ceb7414 | https://github.com/wwjfsfs/wwjyyds/tree/80cd6267fde7cd98838078a0d5178a557ceb7414 |
CuboidPoseHead | import torch
import torch.nn as nn
from torchvision.transforms import functional as F
import torch.nn.functional as F
class CuboidPoseHead(nn.Module):
def __init__(self, beta):
"""Get results from the 3D human pose heatmap. Instead of obtaining
maximums on the heatmap, this module regresses the c... | 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... | chenxinfeng4/mmpose | CuboidPoseHead | false | 12,252 | [
"Apache-2.0"
] | 0 | b0aac4178c1f3d679d2a007e1d9c6c567fc2607d | https://github.com/chenxinfeng4/mmpose/tree/b0aac4178c1f3d679d2a007e1d9c6c567fc2607d |
MVloss | # 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.distribut... | muzammilbehzad/MultiviewTransformer | MVloss | false | 10,562 | [
"MIT"
] | 0 | c6c7c34c8d156e187a986e35268e1fc4a5d0175d | https://github.com/muzammilbehzad/MultiviewTransformer/tree/c6c7c34c8d156e187a986e35268e1fc4a5d0175d |
SpatialGate2d | import torch
import torch.nn as nn
class SpatialGate2d(nn.Module):
def __init__(self, in_channels):
super(SpatialGate2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 1, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
cal = self.conv1(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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | evilidol/kaggle-Steel-Defect-Detection | SpatialGate2d | false | 6,668 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
RLFeatPreprocessNet | # 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... | KaihuaTang/VCTree-Visual-Question-Answering | RLFeatPreprocessNet | false | 8,380 | [
"MIT"
] | 31 | b6b0a8bdb01d45d36de3bded91db42544ad6a593 | https://github.com/KaihuaTang/VCTree-Visual-Question-Answering/tree/b6b0a8bdb01d45d36de3bded91db42544ad6a593 |
LayerNorm | import torch
import torch.nn as nn
import torch.utils.data
class LayerNorm(nn.Module):
"""
Simple 1D LayerNorm.
"""
def __init__(self, features, center=True, scale=False, eps=1e-06):
super().__init__()
self.center = center
self.scale = scale
self.eps = eps
if s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | AndrewPaulChester/sage-code | LayerNorm | false | 32 | [
"MIT"
] | 0 | 9fe676bfbcbc6f642eca29b30a1027fba2a426a0 | https://github.com/AndrewPaulChester/sage-code/tree/9fe676bfbcbc6f642eca29b30a1027fba2a426a0 |
MlpWithAttention | import torch
import torch.nn as nn
class Self_Attn1D(nn.Module):
""" Self attention Layer """
def __init__(self, in_dim, activation, k=8):
super(Self_Attn1D, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv1d(in_channels=in_dim, o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Malta-Lab/IUPE | MlpWithAttention | false | 8,600 | [
"MIT"
] | 10 | 44ddf119917538f02bb69509fec7a8314eed419f | https://github.com/Malta-Lab/IUPE/tree/44ddf119917538f02bb69509fec7a8314eed419f |
ScalarFilter | import torch
import torch as th
import torch.nn as nn
class ScalarFilter(nn.Module):
def __init__(self):
super(ScalarFilter, self).__init__()
def forward(self, p_x, g_x):
"""
input should be scalar: bsz x l1, bsz x l2
return bsz x l2
"""
matrix = g_x.unsqueeze... | 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 |
RKDLoss | # 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.... | RylanSchaeffer/RepDistiller | RKDLoss | false | 5,789 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
EncoderBlock | import torch
import torch.nn as nn
from collections import OrderedDict
class EncoderBlock(nn.Module):
def __init__(self, n_in, n_out, n_layers):
super().__init__()
self.n_in = n_in
self.n_out = n_out
self.n_hid = self.n_out
self.n_layers = n_layers
self.post_gain =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | stankevich-mipt/pixiv-tags-to-image | EncoderBlock | false | 4,384 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
TverskyLoss | import torch
import torch.nn as nn
class TverskyLoss(nn.Module):
"""Tversky Loss.
.. seealso::
Salehi, Seyed Sadegh Mohseni, Deniz Erdogmus, and Ali Gholipour. "Tversky loss function for image segmentation
using 3D fully convolutional deep networks." International Workshop on Machine Learning... | 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... | ivadomed-profile-analysis-project/ivadomed | TverskyLoss | false | 15,652 | [
"MIT"
] | 87 | 3b53e2cb2b210511943da439401e2471fd387876 | https://github.com/ivadomed-profile-analysis-project/ivadomed/tree/3b53e2cb2b210511943da439401e2471fd387876 |
GatedLinear | # 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... | cuiyuhao1996/mmnas | GatedLinear | false | 9,957 | [
"Apache-2.0"
] | 0 | d62e0b3ddc6d15e8f01d0d66367e05fc9691cd3b | https://github.com/cuiyuhao1996/mmnas/tree/d62e0b3ddc6d15e8f01d0d66367e05fc9691cd3b |
SimpleSinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSinModule(torch.nn.Module):
def __init__(self):
super(SimpleSinModule, self).__init__()
def forward(self, a):
return torch.sin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | opti-mix/glow | SimpleSinModule | false | 7,418 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
tofp16 | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class tofp16(nn.Module):
"""
Utility module that implements::
def forward(self, input):
return input.half()
"""
de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data... | DeanChan/apex | tofp16 | false | 9,016 | [
"BSD-3-Clause"
] | 0 | a03267e5e1209f559a6671da56c479a216f418d1 | https://github.com/DeanChan/apex/tree/a03267e5e1209f559a6671da56c479a216f418d1 |
MDN | # 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.... | poctaviano/Handwriting-Model | MDN | false | 4,136 | [
"MIT"
] | 0 | 30311ea0f4cb6e7bc0114cf0b2a96dc915dd9795 | https://github.com/poctaviano/Handwriting-Model/tree/30311ea0f4cb6e7bc0114cf0b2a96dc915dd9795 |
LandmarkHead | import torch
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_ancho... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._utils
from itertools import product as produ... | Capetian/FaceX-Zoo | LandmarkHead | false | 5,026 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
down_right_shifted_conv2d | # 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 ... | elahekhodaie/PixelCnnPP | down_right_shifted_conv2d | false | 10,084 | [
"MIT"
] | 0 | ab1e245ed8c24009364b1f891288eb1a526b0121 | https://github.com/elahekhodaie/PixelCnnPP/tree/ab1e245ed8c24009364b1f891288eb1a526b0121 |
BertGELU | import math
import torch
from torch import nn
class BertGELU(nn.Module):
"""Bert uses GELU as the activation function for the position-wise network.
"""
def forward(self, x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Codle/texar-pytorch | BertGELU | false | 11,298 | [
"Apache-2.0"
] | 0 | d63556e7a8f48076c396467314a771d56552d595 | https://github.com/Codle/texar-pytorch/tree/d63556e7a8f48076c396467314a771d56552d595 |
SoftDiceLossV2 | # 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.cuda.amp as amp
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | imvladikon/pytorch-loss | SoftDiceLossV2 | false | 6,879 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
SmallVDSR_16x | import torch
import torch.nn as nn
def load_param(model1_path, model2):
dict_param1 = torch.load(model1_path)
dict_param2 = dict(model2.named_parameters())
for name2 in dict_param2:
if name2 in dict_param1:
dict_param2[name2].data.copy_(dict_param1[name2].data)
model2.load_state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MingSun-Tse/pytorch-vdsr | SmallVDSR_16x | false | 5,601 | [
"MIT"
] | 1 | 597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 | https://github.com/MingSun-Tse/pytorch-vdsr/tree/597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 |
RepresentationSubspaceDistance | # 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... | neka-nat/Transfer-Learning-Library | RepresentationSubspaceDistance | false | 16,161 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
MemoryUpdater | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Sy-Zhang/recurrent-transformer | MemoryUpdater | false | 11,127 | [
"MIT"
] | 0 | f66ba49a2c9ec42759d3d00d497b49ffe39e18de | https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de |
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