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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SaneBow/AttentionAgentCarRacing | SelfAttention | false | 5,795 | [
"Apache-2.0"
] | 1 | 944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 | https://github.com/SaneBow/AttentionAgentCarRacing/tree/944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 |
FixedSubnetConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.multiprocessing
import torch.nn as nn
import torch.nn.p... | RICE-EIC/Robust_Scratch_Ticket | FixedSubnetConv | false | 8,669 | [
"MIT"
] | 13 | f77b41cdaab6db4922a6d4b5970db75a9bfc7257 | https://github.com/RICE-EIC/Robust_Scratch_Ticket/tree/f77b41cdaab6db4922a6d4b5970db75a9bfc7257 |
MTFullyConnected | # 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 time
import numpy as n... | EXYNOS-999/DrugEx | MTFullyConnected | false | 5,177 | [
"MIT"
] | 1 | f75a90fbc0b9863d594fbff6afecb0f866c076d6 | https://github.com/EXYNOS-999/DrugEx/tree/f75a90fbc0b9863d594fbff6afecb0f866c076d6 |
Affine2D | import torch
import torch.nn as nn
class Affine2D(nn.Module):
def __init__(self, cin):
"""
:param cin:
"""
super(Affine2D, self).__init__()
self.weight = nn.Parameter(torch.ones(1, cin, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, cin, 1, 1))
def forward(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE | Affine2D | false | 9,693 | [
"Apache-2.0"
] | 0 | 13b565c20e59f204151d2dafbd221c7e1b9303c5 | https://github.com/alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE/tree/13b565c20e59f204151d2dafbd221c7e1b9303c5 |
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, math as tl_math
im... | aliasghar53/packnet-sfm | ConvBlock | false | 9,773 | [
"MIT"
] | 0 | d07dcbf026194b618a2bd9fc05b599563611f9a3 | https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3 |
BaseQuantization | # 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 import nn
assert_... | UniSerj/ai-research | BaseQuantization | false | 14,536 | [
"Apache-2.0"
] | 46 | 79f0093c93408cc5dd7d3f56aafd7dc1f901421c | https://github.com/UniSerj/ai-research/tree/79f0093c93408cc5dd7d3f56aafd7dc1f901421c |
CalibrationModel | import torch
class CalibrationModel(torch.nn.Module):
""" Adds temperature scaling parameters to trained model"""
def __init__(self):
super().__init__()
self.temperature = torch.nn.Parameter(torch.ones(1) * 1.5)
def forward(self, logits, device=torch.device('cpu')):
return logits... | 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... | kungfuai/d3m-segmentation-research | CalibrationModel | false | 7,061 | [
"MIT"
] | 1 | 5bc44ddd0e8522fb2b369866ad47aa62a24a8f63 | https://github.com/kungfuai/d3m-segmentation-research/tree/5bc44ddd0e8522fb2b369866ad47aa62a24a8f63 |
TargetContextGate | # 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 ... | NaomiatLibrary/OpenNMT-kpg-release | TargetContextGate | false | 877 | [
"MIT"
] | 0 | 1da3468d7dad22529a77f3526abf9b373bd3dc4c | https://github.com/NaomiatLibrary/OpenNMT-kpg-release/tree/1da3468d7dad22529a77f3526abf9b373bd3dc4c |
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... | cuongpxu/continual-learning | LinearExcitability | false | 1,761 | [
"MIT"
] | 0 | 0f799ddc0efe7e6df7038d2e97303add8d5e01fd | https://github.com/cuongpxu/continual-learning/tree/0f799ddc0efe7e6df7038d2e97303add8d5e01fd |
BiAttention | import torch
from typing import Optional
import torch.nn as nn
from torch.nn.parameter import Parameter
class BiAttention(nn.Module):
def __init__(self, input_size_encoder: 'int', input_size_decoder: 'int',
num_labels: 'int', biaffine: 'bool'=True, **kwargs) ->None:
super(BiAttention, self).__ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | katie0809/KLUE-baseline | BiAttention | false | 3,823 | [
"Apache-2.0"
] | 0 | 144973359e9dc3bbbb3ce7a0cc765b0207f63775 | https://github.com/katie0809/KLUE-baseline/tree/144973359e9dc3bbbb3ce7a0cc765b0207f63775 |
GHMR | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | AlphaLFC/mmdetection | GHMR | false | 4,859 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
LocalizationNet | # 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
impor... | Sanny26/indic-htr | LocalizationNet | false | 5,812 | [
"MIT"
] | 1 | c473573b05c251f6e266cbd69acaa7ab18837f37 | https://github.com/Sanny26/indic-htr/tree/c473573b05c251f6e266cbd69acaa7ab18837f37 |
Conv1d_mp | # 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_... | atosystem/MIDI-BERT | Conv1d_mp | false | 14,911 | [
"MIT"
] | 109 | 61f7efb3be85a2a847e6585237036e052235a6a0 | https://github.com/atosystem/MIDI-BERT/tree/61f7efb3be85a2a847e6585237036e052235a6a0 |
MLP | # 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.autogr... | yifding/W2NER | MLP | false | 13,139 | [
"MIT"
] | 0 | d13128e45f3930a8b8faa794318939dc90a75974 | https://github.com/yifding/W2NER/tree/d13128e45f3930a8b8faa794318939dc90a75974 |
BitEstimator | # 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.functional as F
import t... | Geunwoo-Jeon/iclr_17_compression | BitEstimator | false | 13,725 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
MaxPoolStride1 | import torch
from torch import nn
from torch.nn import functional as F
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(x, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | MaoXianXin/pytorchx | MaxPoolStride1 | false | 11,689 | [
"MIT"
] | 0 | f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | https://github.com/MaoXianXin/pytorchx/tree/f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc |
CORblock_Z | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Identity(nn.Module):
"""
Helper module that stores the current tensor. Useful for accessing by name
"""
def forward(self, x):
return x
class CORblock_Z(nn.Module):
def __init__(self, in_channels, out_channels, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ViCCo-Group/THINGSvision | CORblock_Z | false | 14,561 | [
"MIT"
] | 45 | 27273564631605639287f9b3bd3c57ba8cdb720f | https://github.com/ViCCo-Group/THINGSvision/tree/27273564631605639287f9b3bd3c57ba8cdb720f |
ContrastiveDistanceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
f... | Casyfill/catalyst | ContrastiveDistanceLoss | false | 8,991 | [
"Apache-2.0"
] | 0 | 7f63545dbc53902c3dd959463def28a67a16a989 | https://github.com/Casyfill/catalyst/tree/7f63545dbc53902c3dd959463def28a67a16a989 |
KdCeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KdCeLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits_S, logits_T, temperature=1):
"""
Calculate the cross entropy between logits_S and logits_T
:param logits_S: Tensor of... | 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
... | lonePatient/TorchBlocks | KdCeLoss | false | 15,969 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
PatchEmbed3D | import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
from torch import nn
class PatchEmbed3D(nn.Module):
""" Video to Patch Embedding.
Args:
patch_size (int): Patch token size. Default: (2,4,4).
in_chans (int): Number of input video channels. Default: 3.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | XiaoJake/MTTR | PatchEmbed3D | false | 14,623 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
PolynomialEnvelope | # 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... | krylea/ocp | PolynomialEnvelope | false | 10,491 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 |
soft_L1 | import torch
import torch.utils.data
import torch.nn as nn
class soft_L1(nn.Module):
def __init__(self):
super(soft_L1, self).__init__()
def forward(self, input, target, eps=0.0):
ret = torch.abs(input - target) - eps
ret = torch.clamp(ret, min=0.0, max=100.0)
return ret
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | haidongz-usc/Curriculum-DeepSDF | soft_L1 | false | 15,480 | [
"MIT"
] | 65 | ca216dda8edc6435139a6f657c45800791be94a7 | https://github.com/haidongz-usc/Curriculum-DeepSDF/tree/ca216dda8edc6435139a6f657c45800791be94a7 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
self.wei... | 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_... | CCC-123/ECCVC | L2Norm | false | 11,265 | [
"MIT"
] | 0 | 322009a3423dba831cb3ae4182e7129be3441e70 | https://github.com/CCC-123/ECCVC/tree/322009a3423dba831cb3ae4182e7129be3441e70 |
MyCustomFunctionReluModel | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class MyCustomFunctionReluModel(torch.nn.Module):
def __init__(self):
super().__init__()
class MyReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_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 import triton_helpers
import torch.nn
import torch.onnx
import torch.utils.checkpoint
assert_size_stride = torc... | almiliMSFT/onnxruntime | MyCustomFunctionReluModel | false | 14,801 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
AttnGCNLayer | # 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.... | Roc-Ng/HANet | AttnGCNLayer | false | 8,713 | [
"MIT"
] | 34 | e679703e9e725205424d87f750358fb4f62ceec5 | https://github.com/Roc-Ng/HANet/tree/e679703e9e725205424d87f750358fb4f62ceec5 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
"""
Softmax and sigmoid focal loss
https://github.com/lonePatient/TorchBlocks/blob/master/torchblocks/losses/focal_loss.py
"""
def __init__(self, num_labels, gamma=2.0, alpha=0.25, epsilon=1e-09,
reduction='mean', activation_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 libdevice, math as tl_math
import torc... | okcd00/CDPrototype | FocalLoss | false | 12,851 | [
"MIT"
] | 0 | 5a05b144e3e4b341c1a67fe455f94c01899539d8 | https://github.com/okcd00/CDPrototype/tree/5a05b144e3e4b341c1a67fe455f94c01899539d8 |
WScaleLayer | import torch
import torch.nn as nn
class WScaleLayer(nn.Module):
def __init__(self, size):
super(WScaleLayer, self).__init__()
self.scale = nn.Parameter(torch.randn([1]))
self.b = nn.Parameter(torch.randn(size))
self.size = size
def forward(self, x):
x_size = x.size()... | 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... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | WScaleLayer | false | 7,848 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
InstanceNorm | import torch
import torch.utils.data
import torch
from torch import nn
class InstanceNorm(nn.Module):
def __init__(self, epsilon=1e-08):
super(InstanceNorm, self).__init__()
self.epsilon = epsilon
def forward(self, x):
x = x - torch.mean(x, (2, 3), True)
tmp = torch.mul(x, x)... | 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
from torch import nn
assert_size_stride = ... | siyuhuang/PoseStylizer | InstanceNorm | false | 16,463 | [
"BSD-3-Clause"
] | 75 | d1d832781ddfd3efde24bf32b36a4074fafebcc1 | https://github.com/siyuhuang/PoseStylizer/tree/d1d832781ddfd3efde24bf32b36a4074fafebcc1 |
BiaffineAttention | import torch
from torch import optim as optim
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.utils.checkpoint
class BiaffineAttention(torch.nn.Module):
"""Implements a biaffine attention operator for binary relation classification.
PyTorch ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 optim as optim
import torch.utils.data
import torch.onnx.opera... | Maria-philna/unilm | BiaffineAttention | false | 14,009 | [
"MIT"
] | 5,129 | 5550a335c6d2ae5838b1a90e50cb46f81edcd50f | https://github.com/Maria-philna/unilm/tree/5550a335c6d2ae5838b1a90e50cb46f81edcd50f |
AttentiveStatsPool | import torch
import torch.nn as nn
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SecretKeyTeam/voxceleb_trainer | AttentiveStatsPool | false | 9,552 | [
"MIT"
] | 0 | e235cbc2961d32395d30cf606ee830cd47716383 | https://github.com/SecretKeyTeam/voxceleb_trainer/tree/e235cbc2961d32395d30cf606ee830cd47716383 |
KLDivLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torchvision.trans... | mangye16/Cross-Modal-Re-ID-baseline | KLDivLoss | false | 15,990 | [
"MIT"
] | 249 | 26bc0ce088eb97867ff489dceda386b8092b9fde | https://github.com/mangye16/Cross-Modal-Re-ID-baseline/tree/26bc0ce088eb97867ff489dceda386b8092b9fde |
h_swish | import torch
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
import torch.utils.data
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
i... | DefTruth/PIPNet | h_swish | false | 13,582 | [
"MIT"
] | 162 | a1fb1e229319dac0069e37eb8fb4278d454edbb0 | https://github.com/DefTruth/PIPNet/tree/a1fb1e229319dac0069e37eb8fb4278d454edbb0 |
Gate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | tailerr/R-NET-pytorch | Gate | false | 4,404 | [
"MIT"
] | 0 | a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd | https://github.com/tailerr/R-NET-pytorch/tree/a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd |
DQN | import torch
import torch.nn.functional as F
from torch import nn
class DQN(nn.Module):
def __init__(self, observation_size, action_size, H1=200, H2=160, H3=
120, H4=60):
"""
:param observation_size: Size of belief as defined in belief_agent.py
:param action_size: Model has 1 out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | lilianluong/multitask-card-games | DQN | false | 7,093 | [
"MIT"
] | 1 | ae32e85583c61cc27a44946a6b5fa7c1e2c152ff | https://github.com/lilianluong/multitask-card-games/tree/ae32e85583c61cc27a44946a6b5fa7c1e2c152ff |
RewardModelNetwork | import torch
import torch.nn as nn
import torch.utils.data
class RewardModelNetwork(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super(RewardModelNetwork, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Hcnaeg/DI-engine | RewardModelNetwork | false | 2,393 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
MyConv3d | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyConv3d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=True):
super(MyConv3d, self).__init__()
self.kernel_size = kernel_size
self.conv = nn.Conv3d(in_channels=in_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | xinxindefeiyu/S2VD-master_RESID | MyConv3d | false | 16,742 | [
"MIT"
] | 48 | b075d6873842d70f1d8d3215daf0565f8c0ffe9a | https://github.com/xinxindefeiyu/S2VD-master_RESID/tree/b075d6873842d70f1d8d3215daf0565f8c0ffe9a |
AffineDropPath | import torch
import torch.nn as nn
import torch.utils.data
def drop_path(x, drop_prob=0.0, training=False):
"""
Stochastic Depth per sample.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
mask = keep_prob + to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | yjh0410/actionformer_release | AffineDropPath | false | 16,765 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 |
NormalisedSigmoid | # 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... | hoedt/stable-nalu | NormalisedSigmoid | false | 3,601 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
Oracle | import torch
import torch.nn as nn
class Oracle(nn.Module):
def __init__(self):
super().__init__()
self._criteria = nn.CrossEntropyLoss()
def forward(self, output, y):
y_copy = y.clone()
y_copy[:, 0] += 0.005
return self._criteria(output, y_copy.argmax(dim=1))
def g... | 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
... | yanxurui/portfolio | Oracle | false | 4,605 | [
"MIT"
] | 0 | 032cf47ccac1c5815fd4827bf0d5f3cf43cec990 | https://github.com/yanxurui/portfolio/tree/032cf47ccac1c5815fd4827bf0d5f3cf43cec990 |
FunctionalConv3d | import torch
class FunctionalConv3d(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.conv = torch.nn.Conv3d(*args, **kwargs)
def forward(self, x):
x = torch.nn.functional.conv3d(x, self.conv.weight, self.conv.bias,
self.conv.stride, self.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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tens... | Ilyabasharov/torch2trt | FunctionalConv3d | false | 2,542 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ContrastiveLoss | # 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... | BrunoKM/rhoana_graph_tools | ContrastiveLoss | false | 4,904 | [
"MIT"
] | 1 | 7150f4bc6337ecf51dd9123cf03561a57d655160 | https://github.com/BrunoKM/rhoana_graph_tools/tree/7150f4bc6337ecf51dd9123cf03561a57d655160 |
Precision | # 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... | ChristophReich1996/Cell-DETR | Precision | false | 13,496 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
PreNet | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | airobotnews/cloneVoice | PreNet | false | 14,764 | [
"MIT"
] | 15,983 | 8ad9ba2b60aef57d6d7c83832f07c4f1173d493b | https://github.com/airobotnews/cloneVoice/tree/8ad9ba2b60aef57d6d7c83832f07c4f1173d493b |
SpatialAttention | # 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 ... | Rick-960123/centermask-mdf-master | SpatialAttention | false | 2,759 | [
"BSD-2-Clause"
] | 0 | 49388b03b9ffb06577cd28b9ddaa68cadb82e926 | https://github.com/Rick-960123/centermask-mdf-master/tree/49388b03b9ffb06577cd28b9ddaa68cadb82e926 |
BalancedBinaryCrossEntropyWithLogits | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | hehaoming/RSI-ChangeDetection | BalancedBinaryCrossEntropyWithLogits | false | 6,799 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
BarlowTwinLoss | import torch
import torch.nn.functional as F
def off_diagonal(x):
"""Return a flattened view of the off-diagonal elements of a square matrix.
>>> x = np.array([[1,2,3],[4,5,6],[7,8,9]])
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> x.flatten()
array([1, 2, 3, 4, 5, 6, 7, 8,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | jianzhnie/self_supervised | BarlowTwinLoss | false | 6,950 | [
"Apache-2.0"
] | 1 | d1e0f31ab032150ab0ad007c1e19773135a5fb79 | https://github.com/jianzhnie/self_supervised/tree/d1e0f31ab032150ab0ad007c1e19773135a5fb79 |
FusedDownsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedDownsample | false | 11,628 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
Perceptron | import torch
import torch.nn as nn
import torch.nn.functional as F
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(Perceptron, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_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
import torch.nn as nn
assert_... | Bhaskers-Blu-Org2/PDP-Solver | Perceptron | false | 7,780 | [
"MIT"
] | 28 | 1fca34d81f36268288f46416fb6956e5b36df69e | https://github.com/Bhaskers-Blu-Org2/PDP-Solver/tree/1fca34d81f36268288f46416fb6956e5b36df69e |
Softmax | import torch
import torch.nn as nn
def keep_variance_fn(x):
return x + 0.001
class Softmax(nn.Module):
def __init__(self, dim=1, keep_variance_fn=None):
super(Softmax, self).__init__()
self.dim = dim
self._keep_variance_fn = keep_variance_fn
def forward(self, features_mean, fea... | 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... | collector-m/LiDAR-MOS | Softmax | false | 15,066 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
ExpLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
from scipy.linalg import logm
assert_size_s... | mshakerinava/nflows | ExpLinear | false | 10,578 | [
"MIT"
] | 0 | d86cb1478ff36ffd3e005e980d92a3b0bbffbf02 | https://github.com/mshakerinava/nflows/tree/d86cb1478ff36ffd3e005e980d92a3b0bbffbf02 |
conv2d | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | conv2d | false | 16,654 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
ConvToVector | # 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_... | JannerM/spatial-reasoning | ConvToVector | false | 13,887 | [
"MIT"
] | 54 | e163003a33177e41ca02d5feefee3fdfca5ba154 | https://github.com/JannerM/spatial-reasoning/tree/e163003a33177e41ca02d5feefee3fdfca5ba154 |
BinaryClassificationHead | # 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
assert_size_stride ... | BunnyNoBugs/DeepPavlov | BinaryClassificationHead | false | 12,322 | [
"Apache-2.0"
] | 0 | b2213db633a669d27d6f745dd780530574ccf8b5 | https://github.com/BunnyNoBugs/DeepPavlov/tree/b2213db633a669d27d6f745dd780530574ccf8b5 |
RSubInt | # 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... | ahangchen/torch2trt | RSubInt | false | 6,109 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
TargetQueryDecoderLayer | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
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
from torch._inductor.runtime.... | zhangzhengde0225/SwinTrack | TargetQueryDecoderLayer | false | 16,843 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 |
ClippedLinearQuantization | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Donfa1con/distiller | ClippedLinearQuantization | false | 11,531 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and key... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CurryYuan/X-Trans2Cap | ScaledDotProductAttention | false | 7,937 | [
"Apache-2.0"
] | 11 | c78a27209f14fcbbec74fe8b5edc06faea2e7d44 | https://github.com/CurryYuan/X-Trans2Cap/tree/c78a27209f14fcbbec74fe8b5edc06faea2e7d44 |
ChannelAttentionModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Nitin-Mane/External-Attention-pytorch | ChannelAttentionModule | false | 14,104 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
WeightNet | # 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... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | WeightNet | false | 5,933 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
HardAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
def init_params(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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Danish-VSL/deep-person-reid | HardAttn | false | 13,551 | [
"MIT"
] | 244 | 2e3a4b6706b84c77203f9905683b917ab0871b93 | https://github.com/Danish-VSL/deep-person-reid/tree/2e3a4b6706b84c77203f9905683b917ab0871b93 |
CEFL | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class CEFL(nn.Module):
def __init__(self, gamma=1):
super(CEFL, self).__init__()
self.gamma = gamma
def get_prob(self, input, target):
prob = F.softmax(input, dim=-1)
prob = prob[range(target... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | umairjavaid/staff-employee-classification | CEFL | false | 13,060 | [
"MIT"
] | 0 | fc5fe32acfbde2b188094df90d888eeb0f4f4acd | https://github.com/umairjavaid/staff-employee-classification/tree/fc5fe32acfbde2b188094df90d888eeb0f4f4acd |
RefModel1d | # 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.... | shuohan/pytorch-layers | RefModel1d | false | 4,335 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
BertLayerNorm | import torch
import torch.nn as nn
import torch.cuda
import torch.onnx.utils
import torch.random
import torch.cuda.random
import torch.utils.cpp_extension
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(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.triton_helpers import libdevice
import torch.nn as nn
import torch.cuda
import torch.onnx.utils
import torch.ra... | nict-wisdom/rannc | BertLayerNorm | false | 16,294 | [
"MIT"
] | 45 | a1708807e053e2d58b7f6d6ed925f03aa8504416 | https://github.com/nict-wisdom/rannc/tree/a1708807e053e2d58b7f6d6ed925f03aa8504416 |
Skew | import torch
from torch import nn
class NonSquareError(ValueError):
def __init__(self, name, size):
super().__init__(
'The {} parametrization can just be applied to square matrices. Got a tensor of size {}'
.format(name, size))
class VectorError(ValueError):
def __init__(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | vishalbelsare/geotorch | Skew | false | 16,678 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
ExponentialEnvelope | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | krylea/ocp | ExponentialEnvelope | false | 10,500 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 |
BiAttention | # 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
from torch.nn.parameter import Parameter
assert_size_strid... | katie0809/KLUE-baseline | BiAttention | false | 3,823 | [
"Apache-2.0"
] | 0 | 144973359e9dc3bbbb3ce7a0cc765b0207f63775 | https://github.com/katie0809/KLUE-baseline/tree/144973359e9dc3bbbb3ce7a0cc765b0207f63775 |
Decoder5 | import torch
import torch.nn as nn
class Decoder5(nn.Module):
def __init__(self):
super(Decoder5, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.Upsampling... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | hologerry/wct_experiment | Decoder5 | false | 6,897 | [
"MIT"
] | 1 | 890d885561dc8df8c4ae732aebd902aa838257e6 | https://github.com/hologerry/wct_experiment/tree/890d885561dc8df8c4ae732aebd902aa838257e6 |
AdaptiveTanh | import torch
from torch.nn.parameter import Parameter
class AdaptiveTanh(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
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.triton_helpers import math as tl_math
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._d... | ndem0/PINA | AdaptiveTanh | false | 10,727 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
ReOrgLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
assert_size_stride = torch._C._dynamo.... | Sarathismg/Pose-Estimator-Old-Version | ReOrgLayer | false | 1,023 | [
"Apache-2.0"
] | 0 | ecaa03769323b94a4d7222e2d3606d1ce92a2fae | https://github.com/Sarathismg/Pose-Estimator-Old-Version/tree/ecaa03769323b94a4d7222e2d3606d1ce92a2fae |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 4, (3, 3), 1)
self.dropout2d_1 = nn.Dropout2d(p=0.5)
self.conv2 = nn.Conv2d(4, 32, (3, 3), 1)
self.dropout2d_2 = nn.Dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | nathantau/BigBrain | Net | false | 7,354 | [
"MIT"
] | 1 | b9e81ee3ca91fadeccd59043dcc0062af1e6d365 | https://github.com/nathantau/BigBrain/tree/b9e81ee3ca91fadeccd59043dcc0062af1e6d365 |
SoftmaxAttention | import torch
import torch.nn as nn
def masked_softmax(tensor, mask, kb_mask):
"""
Apply a masked softmax on the last dimension of a tensor.
The input tensor and mask should be of size (batch, *, sequence_length).
Args:
tensor: The tensor on which the softmax function must be applied along
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | TripuraPriyanka/ekmimn | SoftmaxAttention | false | 2,952 | [
"Apache-2.0"
] | 0 | 389c2249369e5b0f09498d79034634ac1db4ff68 | https://github.com/TripuraPriyanka/ekmimn/tree/389c2249369e5b0f09498d79034634ac1db4ff68 |
Mapping | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | 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 |
SiaLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class SiaLoss(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(SiaLoss, se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | zwzhang121/OpenUnReID | SiaLoss | false | 4,678 | [
"Apache-2.0"
] | 0 | 4f399efca3d560c608fb4c9c2ed43f522b17596a | https://github.com/zwzhang121/OpenUnReID/tree/4f399efca3d560c608fb4c9c2ed43f522b17596a |
Pow | import torch
class Pow(torch.nn.Module):
def __init__(self):
super(Pow, self).__init__()
def forward(self, x, y):
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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ahangchen/torch2trt | Pow | false | 6,092 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
LayerNorm | import numbers
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class LayerNorm(nn.Module):
"""Applies Layer Normalization over a mini-batch of inputs as described ... | 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 numbers
import torch.nn as nn
import torch.utils.data
import torch.nn.in... | AbhilashMathews/adahessian | LayerNorm | false | 4,835 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 |
NoiseInjection | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | CurtisASmith/stylegan2-pytorch | NoiseInjection | false | 11,322 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 139ded3394718b9b8a727949dd46ad77ec2ec746 | https://github.com/CurtisASmith/stylegan2-pytorch/tree/139ded3394718b9b8a727949dd46ad77ec2ec746 |
MetaBilinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterp... | RisingStockPrices/multi-shape-siren | MetaBilinear | false | 2,772 | [
"MIT"
] | 0 | f78d6deb94660fd11ef0caf55f88095b74d3e223 | https://github.com/RisingStockPrices/multi-shape-siren/tree/f78d6deb94660fd11ef0caf55f88095b74d3e223 |
PONO | import torch
import torch.nn as nn
class PONO(nn.Module):
def __init__(self, input_size=None, return_stats=False, affine=False,
eps=1e-05):
super(PONO, self).__init__()
self.return_stats = return_stats
self.input_size = input_size
self.eps = eps
self.affine = affin... | 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_... | Boyiliee/PONO | PONO | false | 13,411 | [
"MIT"
] | 133 | b9108e8bf8ba0228635532ba5bdc973b7393d045 | https://github.com/Boyiliee/PONO/tree/b9108e8bf8ba0228635532ba5bdc973b7393d045 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | desmarg/ehr_ml | PositionwiseFeedForward | false | 6,560 | [
"MIT"
] | 1 | 48a385fe2ebdbef655bd4c6b6dd9a73a4e3f76b4 | https://github.com/desmarg/ehr_ml/tree/48a385fe2ebdbef655bd4c6b6dd9a73a4e3f76b4 |
Mish | # 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.nn as nn
assert_size_stride = torch._C._dynamo.gu... | mattroz/yatopi | Mish | false | 3,983 | [
"MIT"
] | 0 | 278bac6f3d2f13916ae9d43309b9f38b608426bd | https://github.com/mattroz/yatopi/tree/278bac6f3d2f13916ae9d43309b9f38b608426bd |
IN_self | # 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_... | EkdeepSLubana/BeyondBatchNorm | IN_self | false | 17,239 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
ImageProcessingModuleAlt | import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageProcessingModuleAlt(nn.Module):
def __init__(self, n_filters):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_filters * 2,
kernel_size=7)
self.conv2 = nn.Conv2d(in_channels=n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bentrevett/task-oriented-language-grounding | ImageProcessingModuleAlt | false | 6,334 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
SimpleDropoutOptimizer | import torch
import torch.nn as nn
class SimpleDropoutOptimizer(nn.Module):
def __init__(self, p):
super().__init__()
if p is not None:
self.dropout = nn.Dropout(p=p)
else:
self.dropout = None
def forward(self, x):
if self.dropout is not 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ArronHZG/ABD-Net | SimpleDropoutOptimizer | false | 9,587 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
SimpleXorModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleXorModule | false | 12,587 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
MarginDisparityDiscrepancy | import torch
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
def shift_log(x: 'torch.Tensor', offset: 'Optional[float]'=1e-06
) ->torch.Tensor:
"""
First shift, then ca... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | mstoelzle/Transfer-Learning-Library | MarginDisparityDiscrepancy | false | 12,885 | [
"MIT"
] | 0 | 7d5022668cbe6d1bedbc7c386d44b9d89c272d6b | https://github.com/mstoelzle/Transfer-Learning-Library/tree/7d5022668cbe6d1bedbc7c386d44b9d89c272d6b |
Conv | import torch
from torch import nn
import torch.utils.data
import torch.nn.init as init
def initial_parameter(net, initial_method=None):
"""A method used to initialize the weights of PyTorch models.
:param net: a PyTorch model
:param str initial_method: one of the following initializations.
-... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LindaCY/fastNLP | Conv | false | 17,624 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | UdbhavPrasad072300/CPS843_Final_Project | MultiHeadAttention | false | 9,731 | [
"MIT"
] | 0 | 042f0bad48c7e49b71ab8efbc4ac5a9e6a6cf31c | https://github.com/UdbhavPrasad072300/CPS843_Final_Project/tree/042f0bad48c7e49b71ab8efbc4ac5a9e6a6cf31c |
PositiveLinear | # 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... | oguzserbetci/monotone-network | PositiveLinear | false | 10,593 | [
"MIT"
] | 0 | 33a317a1dde1a3d3e74dcbe3eb12d1a81e745c95 | https://github.com/oguzserbetci/monotone-network/tree/33a317a1dde1a3d3e74dcbe3eb12d1a81e745c95 |
BoundedSingleVar | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | zweien/idrlnet | BoundedSingleVar | false | 16,833 | [
"Apache-2.0"
] | 66 | 3a19a3301d565c0906aac84ff31eefcff75726a8 | https://github.com/zweien/idrlnet/tree/3a19a3301d565c0906aac84ff31eefcff75726a8 |
TemporalConvNet | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ddcas/singing-language-identification | TemporalConvNet | false | 1,835 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 |
VertexDirectEmbedder | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... | 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
from... | YutouTaro/detectron2 | VertexDirectEmbedder | false | 12,026 | [
"Apache-2.0"
] | 0 | 29f90062fa2978a35f1d599bb30768a2370378ca | https://github.com/YutouTaro/detectron2/tree/29f90062fa2978a35f1d599bb30768a2370378ca |
rotate | import torch
import torch.nn as nn
class rotate(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x90 = x.transpose(2, 3).flip(3)
x180 = x.flip(2).flip(3)
x270 = x.transpose(2, 3).flip(2)
x = torch.cat((x, x90, x180, x270), dim=0)
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... | amonod/udvd | rotate | false | 1,429 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
EncoderUnit | # 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.... | dugusword/transformer | EncoderUnit | false | 6,633 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
psi | # 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... | LuckMonkeys/ATSPrivacy | psi | false | 8,481 | [
"MIT"
] | 14 | 6b580942c6b98b6348d313f2bf90202ec19cefce | https://github.com/LuckMonkeys/ATSPrivacy/tree/6b580942c6b98b6348d313f2bf90202ec19cefce |
BasicModel4_MultiArgs | # 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... | LMdeLiangMi/captum | BasicModel4_MultiArgs | false | 5,471 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
TorchAdd | # 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... | NVIDIA-AI-IOT-private/torch2trt | TorchAdd | false | 10,551 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Block | # 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.... | denisleonov/pytorch-CycleGAN-and-pix2pix | Block | false | 12,280 | [
"BSD-3-Clause"
] | 0 | d1a5f0c5911f70ed896f826619b4067ce737a83d | https://github.com/denisleonov/pytorch-CycleGAN-and-pix2pix/tree/d1a5f0c5911f70ed896f826619b4067ce737a83d |
IOUloss | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | augmentedstartups/EmotionDetectionYoloX | IOUloss | false | 3,138 | [
"Apache-2.0"
] | 0 | 2b0e13b94486a0bd85628f1483a0b710503c2005 | https://github.com/augmentedstartups/EmotionDetectionYoloX/tree/2b0e13b94486a0bd85628f1483a0b710503c2005 |
CrossEntropyLoss | # 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.functi... | RicJM/weighted_c2d | CrossEntropyLoss | false | 14,315 | [
"MIT"
] | 49 | 38053869b77c1544349c53ba6f3c1325254aa413 | https://github.com/RicJM/weighted_c2d/tree/38053869b77c1544349c53ba6f3c1325254aa413 |
DilatedResidualLayer | # 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... | Jaakik/hydra-ml | DilatedResidualLayer | false | 11,535 | [
"MIT"
] | 0 | eae54fc478163130c94450a2a2ddea4f204c1ea9 | https://github.com/Jaakik/hydra-ml/tree/eae54fc478163130c94450a2a2ddea4f204c1ea9 |
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