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 |
|---|---|---|---|---|---|---|---|---|---|---|
RecurrentNeuralRegressor | # 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.... | TheCamusean/sds | RecurrentNeuralRegressor | false | 9,535 | [
"MIT"
] | 0 | 65e1736eb27dcd8829f5bff452fc09ccab3e0ae2 | https://github.com/TheCamusean/sds/tree/65e1736eb27dcd8829f5bff452fc09ccab3e0ae2 |
Invertible1x1Conv | # 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.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | pkulwj1994/normalizing-flows | Invertible1x1Conv | false | 16,296 | [
"MIT"
] | 96 | 326321c4aea4a3f6ab703f82e21277a79cd7d9e4 | https://github.com/pkulwj1994/normalizing-flows/tree/326321c4aea4a3f6ab703f82e21277a79cd7d9e4 |
ModMBStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
import torch.utils.cpp_extension
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
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.distributed as dist
import torch.autograd as... | bladesaber/mmgeneration | ModMBStddevLayer | false | 1,576 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
Scaled_Dot_Product_Attention | # 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.... | Moon-xm/Chinese-Text-Classification-Pytorch | Scaled_Dot_Product_Attention | false | 11,721 | [
"MIT"
] | 0 | 19fe64006418bf4296f884e4d1f038c17b34d3de | https://github.com/Moon-xm/Chinese-Text-Classification-Pytorch/tree/19fe64006418bf4296f884e4d1f038c17b34d3de |
KDETH | import torch
from torch import nn
import torch.nn.functional as F
class KDTH(nn.Module):
"""KD with a Teacher Head auxiliary loss"""
def __init__(self, T=4):
super(KDTH, self).__init__()
self.T = T
def forward(self, y_s, y_t):
y_s_th = y_s[1]
y_s = y_s[0]
p_t = 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Alibaba-MIIL/HeadSharingKD | KDETH | false | 7,696 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
L1 | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | qwopqwop200/Fast-Invertible-Rescaling-Net | L1 | false | 7,518 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
NN | import torch
import torch.nn as nn
import torch.nn.functional as F
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(input_size, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 32)
self.fc4 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | Toygarr/magically-basic-modeling-with-pytorch | NN | false | 11,947 | [
"MIT"
] | 0 | e68b65abcbecbf3eaf4e0e2fb0cf82686811549e | https://github.com/Toygarr/magically-basic-modeling-with-pytorch/tree/e68b65abcbecbf3eaf4e0e2fb0cf82686811549e |
MaximumLikelihoodLoss | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class MaximumLikelihoodLoss(Module):
"""
<a id="MaximumLikelihoodLoss"></a>
## Type II Maximum Likelihood Loss
The distribution $D(\\mathbf{p} ert extcolor{orange}{\\mathbf{lpha}})$ i... | 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 import Module
import torch.utils.data
import torch.nn.funct... | mcx/annotated_deep_learning_paper_implementations | MaximumLikelihoodLoss | false | 7,200 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
softmax | import torch
import torch.nn as nn
class softmax(nn.Module):
def __init__(self, layer=10, channels=32):
super(softmax, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.Softmax(dim=1))
self.layers = nn.Sequential(*layers)
def forward(self, 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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | yifanpu001/PytorchToCaffe | softmax | false | 4,727 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
BatchNormEdge | import torch
import torch.nn as nn
class BatchNormEdge(nn.Module):
"""Batch normalization for edge features.
"""
def __init__(self, hidden_dim):
super(BatchNormEdge, self).__init__()
self.batch_norm = nn.BatchNorm2d(hidden_dim, track_running_stats=False)
def forward(self, e):
... | 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_... | BrandonKates/graph-convnet-tsp | BatchNormEdge | false | 11,260 | [
"MIT"
] | 0 | f6e17e84311c23fd5cab041b7a27b4e0636c44f8 | https://github.com/BrandonKates/graph-convnet-tsp/tree/f6e17e84311c23fd5cab041b7a27b4e0636c44f8 |
SiLU | # 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... | Arui66/YOLOX | SiLU | false | 7,750 | [
"Apache-2.0"
] | 16 | 7ee17936db849600817d7de05269bfdfb1a0eb48 | https://github.com/Arui66/YOLOX/tree/7ee17936db849600817d7de05269bfdfb1a0eb48 |
LRN | import torch
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local... | 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_... | MoriZSJ/GVB | LRN | false | 2,659 | [
"MIT"
] | 0 | 9b954660ef377ead81c8e631c4a0f4a17075b2ea | https://github.com/MoriZSJ/GVB/tree/9b954660ef377ead81c8e631c4a0f4a17075b2ea |
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.... | JLUVicent/DAEGC | GAT | false | 644 | [
"MIT"
] | 0 | 9a4cc50e40e8521fafb00960d1adf8216674c8f6 | https://github.com/JLUVicent/DAEGC/tree/9a4cc50e40e8521fafb00960d1adf8216674c8f6 |
FourLayerSemSegNetWideView | # 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.... | benkoger/kasanka | FourLayerSemSegNetWideView | false | 12,165 | [
"Apache-2.0"
] | 0 | d5b1d32b7abf54845af0832da577137397089001 | https://github.com/benkoger/kasanka/tree/d5b1d32b7abf54845af0832da577137397089001 |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
"""
The discriminator
.. math::
\\begin{equation}
\\mathcal{D}\\left(\\mathbf{h}_{i}^{(r)}, \\mathbf{s}^{(r)}\\right)=\\sigma\\left(\\mathbf{h}_{i}^{(r) T} \\mathbf{M}^{(r)} \\mathbf{s}^{(r)}\\right)
\\end{equation}
... | 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... | BUPTlfq/OpenHGNN | Discriminator | false | 2,008 | [
"Apache-2.0"
] | 0 | 77041e68c33a8a42a2c187c6e42d85b81cbb25d3 | https://github.com/BUPTlfq/OpenHGNN/tree/77041e68c33a8a42a2c187c6e42d85b81cbb25d3 |
GLU | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc / 2)
return x[:, :n... | 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.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | BedirYilmaz/picturate-mwml | GLU | false | 2,017 | [
"MIT"
] | 0 | e0dd1bb9df0e0ee5a9cbefba9ac7ada19a2cc41c | https://github.com/BedirYilmaz/picturate-mwml/tree/e0dd1bb9df0e0ee5a9cbefba9ac7ada19a2cc41c |
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
import torch.backends.cudnn
import torch.utils.data
assert_size_str... | ArmenGhambaryan/kaggle_carvana_segmentation | DiceLoss | false | 13,291 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
outconv | import torch
import torch.nn as nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1, padding_mode='reflect')
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ExplorativeEngineering/LFMNet2 | outconv | false | 13,661 | [
"Apache-2.0"
] | 46 | 3f190be0f047b9e05c69b0a11f99218fd4fc510c | https://github.com/ExplorativeEngineering/LFMNet2/tree/3f190be0f047b9e05c69b0a11f99218fd4fc510c |
MergeLayer | import torch
class MergeLayer(torch.nn.Module):
def __init__(self, dim1, dim2, dim3, dim4):
super().__init__()
self.fc1 = torch.nn.Linear(dim1 + dim2, dim3)
self.fc2 = torch.nn.Linear(dim3, dim4)
self.act = torch.nn.ReLU()
torch.nn.init.xavier_normal_(self.fc1.weight)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Awannaphasch2016/tgn | MergeLayer | false | 89 | [
"Apache-2.0"
] | 0 | a0eb4b4759cb44e053dfb6a825ccac1d54dba33f | https://github.com/Awannaphasch2016/tgn/tree/a0eb4b4759cb44e053dfb6a825ccac1d54dba33f |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karush17/gym-pybullet-drones | Actor | false | 10,316 | [
"MIT"
] | 0 | 7a7acd4f51dcb1cbea8eb9ef0cfcfc7dcf1c90ba | https://github.com/karush17/gym-pybullet-drones/tree/7a7acd4f51dcb1cbea8eb9ef0cfcfc7dcf1c90ba |
PSNRLoss | # 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
import torch.nn as nn
from t... | kshitij12345/kornia | PSNRLoss | false | 7,055 | [
"Apache-2.0"
] | 1 | 4fcc9a570dfa38f67ec812c8fdfabe434b3e466e | https://github.com/kshitij12345/kornia/tree/4fcc9a570dfa38f67ec812c8fdfabe434b3e466e |
Sine | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Bunnycakes62/SIREN | Sine | false | 4,916 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b |
ModulatedToRGB | import torch
import torch.nn as nn
from copy import deepcopy
from functools import partial
from torch.nn import functional as F
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 copy import deepcopy
from functools import partial
fr... | Juggernaut93/mmediting | ModulatedToRGB | false | 13,938 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
Spatial_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
im... | MCC-WH/Token | Spatial_Attention | false | 8,504 | [
"MIT"
] | 30 | eadc301f2df9e1851633be1b63c273659af0da49 | https://github.com/MCC-WH/Token/tree/eadc301f2df9e1851633be1b63c273659af0da49 |
LearnablePositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | gaowanting/paper_code0 | LearnablePositionalEncoding | false | 3,523 | [
"MIT"
] | 0 | 15568fc9989b26df7c582b92163d2f262654712e | https://github.com/gaowanting/paper_code0/tree/15568fc9989b26df7c582b92163d2f262654712e |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | 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.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | ChanLiang/MAP-BERT | ZeroPad1d | false | 261 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
LabelSmoothingCrossEntropyV1 | import torch
from torch import nn
class LabelSmoothingCrossEntropyV1(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""【直接smooth输入logits效果不好】LabelSmoothingCrossEntropy, no-softmax-input
eps==0-1, 通过控制ce权重、新增后置项来处理来平滑
urls: [pytorch | labelSmooth](https://zhu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | dumpmemory/Pytorch-NLU | LabelSmoothingCrossEntropyV1 | false | 15,258 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
CLNLayer | # 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.fun... | xh-liu-tech/CIPS-3D | CLNLayer | false | 11,109 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target):
smooth = 1.0
intersection = (input * target).sum()
return 1 - (2.0 * intersection + smooth) / (input.sum() + 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | MichalBusta/OpenCitiesAIC | DiceLoss | false | 17,719 | [
"MIT"
] | 7 | 2358118a782edde27a588d6adaf79941cbd90de6 | https://github.com/MichalBusta/OpenCitiesAIC/tree/2358118a782edde27a588d6adaf79941cbd90de6 |
TensorMax | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/pipelinex | TensorMax | false | 14,037 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
ArcFaceLoss | # 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 math... | CTPLab/IID_representation_learning | ArcFaceLoss | false | 4,951 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
MLP | import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import *
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.fc2 = nn.Linear(512, 128)
self.fc3 = nn.Linear(128, 10)
def forward(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 import triton_helpers
from torch._inductor.runtime.... | nox-410/nnfusion | MLP | false | 16,194 | [
"MIT"
] | 639 | 0777e297299c4e7a5071dc2ee97b87adcd22840e | https://github.com/nox-410/nnfusion/tree/0777e297299c4e7a5071dc2ee97b87adcd22840e |
StdConv2dSame | import math
import torch
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.data.distributed
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k, s, 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.triton_helpers import libdevice
import math
import ... | ziniuwan/maed | StdConv2dSame | false | 16,845 | [
"MIT"
] | 145 | 9e1f1c37eba81da86c8d9c62dc9be41a01abff5b | https://github.com/ziniuwan/maed/tree/9e1f1c37eba81da86c8d9c62dc9be41a01abff5b |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, n_obs, output_dim, hidden_size, init_w=0.003):
super(Critic, self).__init__()
self.linear1 = nn.Linear(n_obs + output_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | KhalilWong/Learn-RL | Critic | false | 2,462 | [
"MIT"
] | 0 | 9f63c5adafab1413362366d28d8711096ce6648c | https://github.com/KhalilWong/Learn-RL/tree/9f63c5adafab1413362366d28d8711096ce6648c |
ConvUnit | # 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... | juingzhou/Base-on-PyTorch-implementation-CapsuleNet | ConvUnit | false | 10,319 | [
"MIT"
] | 0 | 6b030bf93b258d9d6496379bcbe4b94542366817 | https://github.com/juingzhou/Base-on-PyTorch-implementation-CapsuleNet/tree/6b030bf93b258d9d6496379bcbe4b94542366817 |
SimilarityMatrix | import torch
import torch.utils.data
class SimilarityMatrix(torch.nn.Module):
def __init__(self, padding=0):
super().__init__()
self.padding = padding
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
assert type(query_embed) == type(doc_embed)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AlexWang000/capreolus | SimilarityMatrix | false | 4,823 | [
"Apache-2.0"
] | 1 | 00b0bf471ea0eb116ab973254ea61b0492405c54 | https://github.com/AlexWang000/capreolus/tree/00b0bf471ea0eb116ab973254ea61b0492405c54 |
convnet | # 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_... | lzz0007/pyGAT | convnet | false | 3,957 | [
"MIT"
] | 0 | ea09c56037185ec5924dcd20b9c09d151174d1a3 | https://github.com/lzz0007/pyGAT/tree/ea09c56037185ec5924dcd20b9c09d151174d1a3 |
DPRNNCell | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | anibadde/opacus | DPRNNCell | false | 14,860 | [
"Apache-2.0"
] | 958 | be221231e1b579bdae4ad34c8ae0c7c4928cee25 | https://github.com/anibadde/opacus/tree/be221231e1b579bdae4ad34c8ae0c7c4928cee25 |
h_sigmoid | # 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... | CYHYCY/voice-classification | h_sigmoid | false | 17,074 | [
"Apache-2.0"
] | 8 | a6f62e2f1c39b08323da3632411f4ba6b04d5f37 | https://github.com/CYHYCY/voice-classification/tree/a6f62e2f1c39b08323da3632411f4ba6b04d5f37 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | rcaborges/music-cold-start | Net | false | 4,184 | [
"Apache-2.0"
] | 0 | a2b321e8b5ef7b894b5e0659c5da2f9ae3df25d8 | https://github.com/rcaborges/music-cold-start/tree/a2b321e8b5ef7b894b5e0659c5da2f9ae3df25d8 |
WRNBottleneck | import torch
import torch.utils.data
import torch.nn as nn
def wrn_conv1x1(in_channels, out_channels, stride, activate):
"""
1x1 version of the WRN specific convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | HyperGAN/imgclsmob | WRNBottleneck | false | 17,683 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
CommunicationLayer | # 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... | jaketae/res-mlp | CommunicationLayer | false | 12,599 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
BetaVAE_H | # 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.... | KinWaiCheuk/Beta-VAE | BetaVAE_H | false | 5,531 | [
"MIT"
] | 1 | 57f538320fed76b54e8489656b11dc83c06d1584 | https://github.com/KinWaiCheuk/Beta-VAE/tree/57f538320fed76b54e8489656b11dc83c06d1584 |
ScaleReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | hilman-dayo/ObjectDetection-OneStageDet | ScaleReLU | false | 15,517 | [
"MIT"
] | 331 | 44054ad335e24e99a98fdad0d18b9bf3a80c941c | https://github.com/hilman-dayo/ObjectDetection-OneStageDet/tree/44054ad335e24e99a98fdad0d18b9bf3a80c941c |
GMoF | # 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... | MoyGcc/hpcwild | GMoF | false | 14,068 | [
"MIT"
] | 47 | 8ed35c3f188284af2a4dd0d68b09fbceb105c2ba | https://github.com/MoyGcc/hpcwild/tree/8ed35c3f188284af2a4dd0d68b09fbceb105c2ba |
GlobalLayerNorm | # 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
from itertools import product as product
assert_size_stri... | slapshin/TalkNet_ASD | GlobalLayerNorm | false | 4,356 | [
"MIT"
] | 0 | 343fac5c8d2bef2b98244e3acf20ac322711a4c7 | https://github.com/slapshin/TalkNet_ASD/tree/343fac5c8d2bef2b98244e3acf20ac322711a4c7 |
_MixPool2d | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | x6rulin/TP-GAN | _MixPool2d | false | 4,577 | [
"MIT"
] | 0 | 1716cf06aaff8a6a2cee2548ec662dcdd68c0449 | https://github.com/x6rulin/TP-GAN/tree/1716cf06aaff8a6a2cee2548ec662dcdd68c0449 |
MultiheadAttention | import torch
import torch.nn as nn
import torch.utils.data.distributed
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
em... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | cocopambag/insightface | MultiheadAttention | false | 3,307 | [
"MIT"
] | 0 | c33102e4844520cda6c2b3df63278aed935e2f4e | https://github.com/cocopambag/insightface/tree/c33102e4844520cda6c2b3df63278aed935e2f4e |
GaussianFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | JaguAroo/SRResCGAN | GaussianFilter | false | 597 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
Attention | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Norm(nn.Module):
def __init__(self, dim_seq, input_size, eps=1e-06):
super().__init__()
self.size = input_size
self.seq = dim_seq
self.alpha = nn.Parameter(torch.ones((self.size, self.seq))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | mingweima/hintplaygame | Attention | false | 4,013 | [
"MIT"
] | 0 | 31f35a22111a2e5e7e5d8e90f92326bc784c5fe7 | https://github.com/mingweima/hintplaygame/tree/31f35a22111a2e5e7e5d8e90f92326bc784c5fe7 |
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... | LuckMonkeys/ATSPrivacy | MedianPool2d | false | 8,474 | [
"MIT"
] | 14 | 6b580942c6b98b6348d313f2bf90202ec19cefce | https://github.com/LuckMonkeys/ATSPrivacy/tree/6b580942c6b98b6348d313f2bf90202ec19cefce |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class BaseAttention(nn.Module):
def __init__(self):
super().__init__()
def forward(self, *args, **kwargs):
raise NotImplementedError
class ScaledDotProductAttention(BaseAttention):
"""Scaled dot-produ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ROBINADC/BiGRU-CRF-with-Attention-for-NER | ScaledDotProductAttention | false | 8,712 | [
"MIT"
] | 27 | b9e037ebd6e1d56500ffb60c6030013982c17ded | https://github.com/ROBINADC/BiGRU-CRF-with-Attention-for-NER/tree/b9e037ebd6e1d56500ffb60c6030013982c17ded |
GlobalAttention | import torch
import torch.distributed
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_as(le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | GraphGrailAi/summ-abs-dev | GlobalAttention | false | 2,321 | [
"MIT"
] | 0 | 512f253bf72b6529589b29d06959b560b79f1cde | https://github.com/GraphGrailAi/summ-abs-dev/tree/512f253bf72b6529589b29d06959b560b79f1cde |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LQNew/LWDRL | Critic | false | 8,420 | [
"MIT"
] | 11 | 0e4fab077a0cfbd27590b840557f4fda033c74ff | https://github.com/LQNew/LWDRL/tree/0e4fab077a0cfbd27590b840557f4fda033c74ff |
DCCWeightedELoss | # 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
import torch.nn as nn
import... | ankmathur96/torchsupport | DCCWeightedELoss | false | 3,164 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | keonlee9420/DiffSinger | ResidualBlock | false | 15,828 | [
"MIT"
] | 95 | 2bfcae4a78068c2061eae64ee675959a077aa54b | https://github.com/keonlee9420/DiffSinger/tree/2bfcae4a78068c2061eae64ee675959a077aa54b |
TokenMixer | import torch
import torch.nn.functional as F
from torch import nn
class FeedForward(nn.Module):
def __init__(self, num_features, expansion_factor, dropout):
super().__init__()
num_hidden = expansion_factor * num_features
self.fc1 = nn.Linear(num_features, num_hidden)
self.fc2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | Misuzu-Kurenai/mlp-singer | TokenMixer | false | 856 | [
"MIT"
] | 0 | 416451045bb9b3965aaf496e84a8b45332a6ba59 | https://github.com/Misuzu-Kurenai/mlp-singer/tree/416451045bb9b3965aaf496e84a8b45332a6ba59 |
SppPooling | import torch
import torch as t
import torch.nn as nn
class SppPooling(nn.Module):
def __init__(self, levels=[1, 2, 4]):
super(SppPooling, self).__init__()
self.Pools = nn.ModuleList([nn.AdaptiveMaxPool2d((i, i)) for i in
levels])
def forward(self, x):
assert len(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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Asichurter/Few-Shot-Project | SppPooling | false | 75 | [
"MIT"
] | 0 | 865cd6aa7b996c518dfa48dcc9ffad90445f9efe | https://github.com/Asichurter/Few-Shot-Project/tree/865cd6aa7b996c518dfa48dcc9ffad90445f9efe |
VariantSigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | entn-at/AGAIN-VC | VariantSigmoid | false | 15,307 | [
"MIT"
] | 78 | dbf94bf55882f897c312c7760cd892c51c93c9ab | https://github.com/entn-at/AGAIN-VC/tree/dbf94bf55882f897c312c7760cd892c51c93c9ab |
SRNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | MayankSingal/PyTorch-Zero-Shot-Super-Resolution | SRNet | false | 5,590 | [
"MIT"
] | 1 | 3521b02fd338fc90eef88c551a8bed4afc54c8c6 | https://github.com/MayankSingal/PyTorch-Zero-Shot-Super-Resolution/tree/3521b02fd338fc90eef88c551a8bed4afc54c8c6 |
UpSample | # 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 import nn
import t... | Aarsh2001/annotated_deep_learning_paper_implementations | UpSample | false | 4,781 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
MultiHeadAttention | import torch
import numpy as np
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
scaled_attention_logits += mask * -1000000000.0
attention_weights = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | depengchen123/ctrl | MultiHeadAttention | false | 15,184 | [
"BSD-3-Clause"
] | 1,559 | 8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 | https://github.com/depengchen123/ctrl/tree/8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 |
ConvAutoencoder | # 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... | AlexMetsai/pytorch-time-series-autoencoder | ConvAutoencoder | false | 11,167 | [
"MIT"
] | 0 | 460e364edcb7c7a84d2e544a22cd48f51cdda4aa | https://github.com/AlexMetsai/pytorch-time-series-autoencoder/tree/460e364edcb7c7a84d2e544a22cd48f51cdda4aa |
BaseCNN | # 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... | PandoraLS/SpeechEnhancement | BaseCNN | false | 17,794 | [
"MIT"
] | 6 | f548eaafbe524a40c8cfd2221f7adf3a444b7a7d | https://github.com/PandoraLS/SpeechEnhancement/tree/f548eaafbe524a40c8cfd2221f7adf3a444b7a7d |
AmdimNCELoss | # 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.... | SNUHDR2018/ConSSL | AmdimNCELoss | false | 14,347 | [
"MIT"
] | 78 | c7d406d0224e38895986c8fb7281a189e493c982 | https://github.com/SNUHDR2018/ConSSL/tree/c7d406d0224e38895986c8fb7281a189e493c982 |
ActorCriticMLP | # 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.... | Benjamin-Etheredge/lightning-bolts | ActorCriticMLP | false | 148 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
ShuffleBlock | # 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... | CYHYCY/cifar10 | ShuffleBlock | false | 4,952 | [
"Apache-2.0"
] | 1 | 37254801045b76604a922884da87744aeb99b416 | https://github.com/CYHYCY/cifar10/tree/37254801045b76604a922884da87744aeb99b416 |
GlobalAttentionGeneral | # 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.... | ts170/T2I_CL | GlobalAttentionGeneral | false | 10,911 | [
"MIT"
] | 0 | 8754bea1101aabcbf8108b95e722f7aaeb385869 | https://github.com/ts170/T2I_CL/tree/8754bea1101aabcbf8108b95e722f7aaeb385869 |
DiceLoss | import torch
from torch import nn
import torch.nn.functional as F
import torch._utils
class BinaryDiceLoss(nn.Module):
"""Dice loss of binary class
Args:
smooth: A float number to smooth loss, and avoid NaN error, default: 1
p: Denominator value: \\sum{x^p} + \\sum{y^p}, default: 2
pre... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | ilcessadecalcular/segmentation | DiceLoss | false | 10,601 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-05)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Willy0919/progressive-coordinate-transforms | StdConv2d | false | 14,591 | [
"Apache-2.0",
"MIT"
] | 142 | b637fa2541a815d270e162a4c9cd3348b098d48a | https://github.com/Willy0919/progressive-coordinate-transforms/tree/b637fa2541a815d270e162a4c9cd3348b098d48a |
BCELoss2d | # 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
from torch.... | chicm/carvana | BCELoss2d | false | 1,686 | [
"Apache-2.0"
] | 0 | 493a19fbb2fdab1cc1b4b95d97742684e4144229 | https://github.com/chicm/carvana/tree/493a19fbb2fdab1cc1b4b95d97742684e4144229 |
Attention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, opt):
super(Attention, self).__init__()
self.lin_u = nn.Linear(opt['hidden_dim'], opt['hidden_dim'])
self.lin_v = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 | Attention | false | 15,003 | [
"MIT"
] | 57 | ed54c20523a5b3f295b90a9c08f7c54e8258d04a | https://github.com/caojiangxia/BiGI/tree/ed54c20523a5b3f295b90a9c08f7c54e8258d04a |
ClipL1 | import torch
import torch.nn as nn
class ClipL1(nn.Module):
""" Clip L1 loss
From: https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/
ClipL1 Loss combines Clip function and L1 loss. self.clip_min sets the
gradients of well-trained pixels to zeros and clip_max works as a noise filter.
da... | 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
... | grofit/traiNNer | ClipL1 | false | 15,457 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
Mean | # 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
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Donfa1con/distiller | Mean | false | 11,526 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
ActNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride =... | Schwartz-Zha/My-invertible-resnet | ActNorm | false | 1,036 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
CustomizedNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | cabuliwallah/analytics-zoo | CustomizedNet | false | 6,375 | [
"Apache-2.0"
] | 1 | 5e662bd01c5fc7eed412973119594cf2ecea8b11 | https://github.com/cabuliwallah/analytics-zoo/tree/5e662bd01c5fc7eed412973119594cf2ecea8b11 |
TransformerNet | # 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.... | Arjuna197/examples | TransformerNet | false | 11,449 | [
"BSD-3-Clause"
] | 0 | f504ea2aafc8a8baa5effb659fc1c20a70aabdda | https://github.com/Arjuna197/examples/tree/f504ea2aafc8a8baa5effb659fc1c20a70aabdda |
TensorClampMin | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ahangchen/torch2trt | TensorClampMin | false | 6,107 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, 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 | ScaledDotProductAttention | false | 14,117 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
MultiheadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import torch.utils.checkpoint
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Wang-Chuanyu/MMSA | MultiheadAttention | false | 5,967 | [
"MIT"
] | 1 | 2a720530c369e68656102287edb651780e827135 | https://github.com/Wang-Chuanyu/MMSA/tree/2a720530c369e68656102287edb651780e827135 |
InvertibleDownsampling1D | from torch.autograd import Function
import torch
import numpy as np
from warnings import warn
from typing import Union
from typing import Tuple
from torch.nn.common_types import _size_1_t
from torch.nn.modules.utils import _single
import torch.nn.functional as F
def _cayley(A):
I = torch.eye(A.shape[-1], device=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.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleDownsampling1D | false | 15,032 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
HouseHolderFlow | import torch
import torch.utils.data
import torch.nn as nn
class HouseHolderFlow(nn.Module):
def forward(self, v, z):
"""
:param v: batch_size (B) x latent_size (L)
:param z: batch_size (B) x latent_size (L)
:return: z_new = z - 2* v v_T / norm(v,2) * z
"""
vvT = t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | BratChar/variational-item-response-theory-public | HouseHolderFlow | false | 13,404 | [
"MIT"
] | 52 | 12862157e99506a0ed7018f1b8a485d4e61fb5bf | https://github.com/BratChar/variational-item-response-theory-public/tree/12862157e99506a0ed7018f1b8a485d4e61fb5bf |
RLFeatPreprocessNet | import torch
import torch.nn as nn
import torch.utils.data
class RLFeatPreprocessNet(nn.Module):
"""
Preprocess Features
1. visual feature
2. label prediction embed feature
3. box embed
4. overlap embed
"""
def __init__(self, feat_size, embed_size, bbox_size, overlap_size,
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
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 |
BiLinearSim | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class BiLinearSim(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.linear = torch.nn.Linear(config.hidden_size, config.
hidden_size, bias=False)
def forward(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.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | kiminh/mt-dnn | BiLinearSim | false | 7,022 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
residualUnit | # 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.... | ginobilinie/medSynthesisV1 | residualUnit | false | 15,432 | [
"MIT"
] | 166 | 1fd202c5928466ef9b11cfebc4490341899312e7 | https://github.com/ginobilinie/medSynthesisV1/tree/1fd202c5928466ef9b11cfebc4490341899312e7 |
LRN | import torch
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=
False):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if self.ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | zenghui9977/AFL | LRN | false | 13,165 | [
"MIT"
] | 0 | 769d78be94ce8f80d376aceb2de9dc5a9d20a807 | https://github.com/zenghui9977/AFL/tree/769d78be94ce8f80d376aceb2de9dc5a9d20a807 |
OhemCELoss2D | import math
import torch
import torch.nn as nn
class OhemCELoss2D(nn.CrossEntropyLoss):
"""2D Cross Entropy Loss with Auxilary Loss"""
def __init__(self, n_min, thresh=0.7, ignore_index=-1):
super(OhemCELoss2D, self).__init__(None, None, ignore_index,
reduction='none')
self.thresh... | 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 math
import tor... | dani3l125/TDNet | OhemCELoss2D | false | 15,115 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
segmentation_layer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class segmentation_layer(nn.Module):
def __init__(self, args):
super(segmentation_layer, self).__init__()
self.segm_layer = nn.Conv2d(32, args.snumclass, kernel_size=1)
def forward(self, featMap):
segm =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zhenpeiyang/RelativePose | segmentation_layer | false | 16,815 | [
"BSD-3-Clause"
] | 144 | 2e9fdf5003c5952cf610f8c6d891519b9e9e014b | https://github.com/zhenpeiyang/RelativePose/tree/2e9fdf5003c5952cf610f8c6d891519b9e9e014b |
ResBlock1D | # 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.... | liuruoze/mini-AlphaStar | ResBlock1D | false | 15,933 | [
"Apache-2.0"
] | 108 | cf9de2507d526a5fb8ef67676aab2ffb92738640 | https://github.com/liuruoze/mini-AlphaStar/tree/cf9de2507d526a5fb8ef67676aab2ffb92738640 |
HR2O_NL | # 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.... | AlexandreDh/ACAR-Net | HR2O_NL | false | 13,472 | [
"Apache-2.0"
] | 162 | db28009388512e31cb6ff8e86725dc9b026886b6 | https://github.com/AlexandreDh/ACAR-Net/tree/db28009388512e31cb6ff8e86725dc9b026886b6 |
ConcreteDenseMixture | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libd... | jiwoncpark/fast-forward | ConcreteDenseMixture | false | 10,448 | [
"MIT"
] | 0 | 640a521241a8756be2a0d42282e88d56a2290fca | https://github.com/jiwoncpark/fast-forward/tree/640a521241a8756be2a0d42282e88d56a2290fca |
ConvDownsample2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Iceland-Leo/StyleGAN2_PyTorch | ConvDownsample2d | false | 5,337 | [
"MIT"
] | 1 | 3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 | https://github.com/Iceland-Leo/StyleGAN2_PyTorch/tree/3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 |
GCN_encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | bwalker1/graph-generation | GCN_encoder | false | 10,015 | [
"MIT"
] | 0 | e068769cb021760eb2549ced382b1a217609db86 | https://github.com/bwalker1/graph-generation/tree/e068769cb021760eb2549ced382b1a217609db86 |
AconC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Aditya239233/MDP | AconC | false | 16,900 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
partCE | import torch
import torch.nn as nn
import torch.utils.data
class partCE(nn.Module):
def __init__(self, if_average=False):
super(partCE, self).__init__()
self.crit = nn.CrossEntropyLoss(size_average=if_average)
self.maximum_score = 100000
def forward(self, scores, target):
par... | 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
... | INK-USC/shifted-label-distribution | partCE | false | 8,260 | [
"Apache-2.0"
] | 37 | 3cf2b7ced3b2e18234db405f6014f049c4830d71 | https://github.com/INK-USC/shifted-label-distribution/tree/3cf2b7ced3b2e18234db405f6014f049c4830d71 |
ConvertPointsFromHomogeneous | import torch
import torch.nn as nn
def convert_points_from_homogeneous(points):
"""Function that converts points from homogeneous to Euclidean space.
See :class:`~torchgeometry.ConvertPointsFromHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tg... | 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... | LucaswasTaken/frankmocap | ConvertPointsFromHomogeneous | false | 783 | [
"BSD-3-Clause"
] | 0 | 17c1761326991d0faab58bd10888e9043abf6bd5 | https://github.com/LucaswasTaken/frankmocap/tree/17c1761326991d0faab58bd10888e9043abf6bd5 |
Conv3DBlock | # 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 typing import Union
assert_size_stride = torch._C._dy... | rll-research/ARM | Conv3DBlock | false | 16,328 | [
"BSD-3-Clause"
] | 46 | 7a51e00fabdcdbd8ad2b235266c66115e79deeb0 | https://github.com/rll-research/ARM/tree/7a51e00fabdcdbd8ad2b235266c66115e79deeb0 |
Linear | # 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
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | guoyongcs/HNAS | Linear | false | 15,478 | [
"MIT"
] | 60 | 2b34e1b637bb03d23ca6559c1b5d1245d9744348 | https://github.com/guoyongcs/HNAS/tree/2b34e1b637bb03d23ca6559c1b5d1245d9744348 |
HardTripletLoss | # 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.... | Shubodh/NetVLAD-pytorch | HardTripletLoss | false | 9,493 | [
"MIT"
] | 0 | ea45bac16dbb3e3bec4172df58715bf3526ee502 | https://github.com/Shubodh/NetVLAD-pytorch/tree/ea45bac16dbb3e3bec4172df58715bf3526ee502 |
TwoLayerNet | import torch
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h_relu = self.linear1(x).clamp(min=0)
y_pred... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Saran-nns/delve | TwoLayerNet | false | 1,015 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
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