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 |
|---|---|---|---|---|---|---|---|---|---|---|
chroma_subsampling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Liamkuo/SAIR | chroma_subsampling | false | 17,573 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
KLDivergence | # 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 ... | HIT-cwh/mmrazor | KLDivergence | false | 13,743 | [
"Apache-2.0"
] | 553 | 2dad24044d7f1dad88f20221f8fc071dd40fdd4f | https://github.com/HIT-cwh/mmrazor/tree/2dad24044d7f1dad88f20221f8fc071dd40fdd4f |
My_loss | # 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 as nn
i... | wtomin/MIMA-Net | My_loss | false | 16,732 | [
"MIT"
] | 58 | c0330777313ac04b25e53b137dbecd78b5c8dde6 | https://github.com/wtomin/MIMA-Net/tree/c0330777313ac04b25e53b137dbecd78b5c8dde6 |
ScalarMix | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | attardi/parser | ScalarMix | false | 1,490 | [
"MIT"
] | 0 | 1978ba94ba649ad0a723d71bb2ca225c7e705702 | https://github.com/attardi/parser/tree/1978ba94ba649ad0a723d71bb2ca225c7e705702 |
TripletMarginLoss | # 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... | jce2090/palmprint-recognition | TripletMarginLoss | false | 3,713 | [
"MIT"
] | 0 | d2d93c6817afe1b67650dae6516a3d180aaeca38 | https://github.com/jce2090/palmprint-recognition/tree/d2d93c6817afe1b67650dae6516a3d180aaeca38 |
AffineConstantFlow | import torch
import torch.nn as nn
class AffineConstantFlow(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super()._... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | LamaLenny/DeepGenerativeModels | AffineConstantFlow | false | 2,648 | [
"MIT"
] | 0 | c2a40e4e71af844f8357da5267b1d017f762a235 | https://github.com/LamaLenny/DeepGenerativeModels/tree/c2a40e4e71af844f8357da5267b1d017f762a235 |
BertPreTrainingHeads | # 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 ... | PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st | BertPreTrainingHeads | false | 17,789 | [
"Apache-2.0"
] | 4 | 6382433cda69c655f03c3cc284dc076407f18dc9 | https://github.com/PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st/tree/6382433cda69c655f03c3cc284dc076407f18dc9 |
FeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_mo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | AviVarma/torchASN-Transformer | FeedForward | false | 88 | [
"MIT"
] | 0 | 55bccf4cdb099cd8e9ac99f5f87f989ce2add983 | https://github.com/AviVarma/torchASN-Transformer/tree/55bccf4cdb099cd8e9ac99f5f87f989ce2add983 |
FocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | wanghao15536870732/plants_disease_classify | FocalLoss | false | 4,521 | [
"Apache-2.0"
] | 0 | 6d0d1d39f0ec15fc2bd523142c5c403a1577da84 | https://github.com/wanghao15536870732/plants_disease_classify/tree/6d0d1d39f0ec15fc2bd523142c5c403a1577da84 |
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
from torch._inductor.runtime.... | blockide/Chess-ML | Net | false | 12,198 | [
"MIT"
] | 0 | 3b1572f715ed710f5ce240c76bb79ae8f186f32a | https://github.com/blockide/Chess-ML/tree/3b1572f715ed710f5ce240c76bb79ae8f186f32a |
TreeLSTM | # 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 ... | Devin-Taylor/pytorch-dynamic-batching-benchmark | TreeLSTM | false | 2,177 | [
"Apache-2.0"
] | 0 | aaf913b13a77a2898dfdf8d92cd25b01789a548a | https://github.com/Devin-Taylor/pytorch-dynamic-batching-benchmark/tree/aaf913b13a77a2898dfdf8d92cd25b01789a548a |
ps_FNNDenoiser | from torch.nn import Module
import torch
from torch.nn import Linear
from torch.nn.init import xavier_normal_
from torch.nn.functional import relu
class ps_FNNDenoiser(Module):
def __init__(self, input_dim):
"""The FNN enc and FNN dec of the Denoiser.
:param input_dim: The input dimensionality.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.nn import Module
f... | ddcas/singing-language-identification | ps_FNNDenoiser | false | 1,817 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 |
SimpleLeakyReluModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLeakyReluModule(torch.nn.Module):
def __init__(self, negative_slope=0.01, inplace=False):
super(SimpleLeakyReluModule, self).__init__()
self.negative_slope = negative_slope
self.inplace = inplace
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleLeakyReluModule | false | 14,675 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self, gen_out_dim):
super().__init__()
self.l1 = torch.nn.Linear(gen_out_dim, 256)
self.l2 = torch.nn.Linear(256, 256)
self.l3 = torch.nn.Linear(256, 256)
self.l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Phutoast/Win-or-Learn-Fast | Discriminator | false | 17,807 | [
"MIT"
] | 7 | 5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 | https://github.com/Phutoast/Win-or-Learn-Fast/tree/5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 |
BasicModel2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | aravipati12/captum | BasicModel2 | false | 10,093 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
DiceLossWithLogits | import torch
import torch.nn as nn
import torch.utils.data
def flatten_samples(input_):
"""
Flattens a tensor or a variable such that the channel axis is first and the sample axis
is second. The shapes are transformed as follows:
(N, C, H, W) --> (C, N * H * W)
(N, C, D, H, W) --> (C, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | JonasHell/torch-em | DiceLossWithLogits | false | 8,383 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
SKL | import torch
import torch.nn as nn
import torch.nn.functional as F
class SKL(nn.Module):
def __init__(self, epsilon=1e-08):
super(SKL, self).__init__()
self.epsilon = epsilon
def forward(self, input, target):
logit = input.view(-1, input.size(-1)).float()
target = target.view... | 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 | SKL | false | 15,960 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, dim_input, dim_output):
super(Critic, self).__init__()
self._dim_input = dim_input
self._dim_output = dim_output
H_LAYER1 = 50
H_LAYER2 = 20
self.linear1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | cheng-xie/dpgfddagger | Critic | false | 3,283 | [
"MIT"
] | 0 | 5264d5b9e0ab76fc9620da63bcfd78b25dadcbec | https://github.com/cheng-xie/dpgfddagger/tree/5264d5b9e0ab76fc9620da63bcfd78b25dadcbec |
TemporalAttentionLayer | import torch
import torch.nn as nn
class TemporalAttentionLayer(nn.Module):
"""Single Graph Temporal Attention Layer
:param n_features: number of input features/nodes
:param window_size: length of the input sequence
:param dropout: percentage of nodes to dropout
:param alpha: negative slope used i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | lawson-source/mtad-gat-pytorch | TemporalAttentionLayer | false | 15,879 | [
"MIT"
] | 93 | 9e671ea99dedd82ac55f53e53af1d1b56c13ebff | https://github.com/lawson-source/mtad-gat-pytorch/tree/9e671ea99dedd82ac55f53e53af1d1b56c13ebff |
MultiHeadAttention | import math
import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Oreoluwa1234/NeMo | MultiHeadAttention | false | 9,717 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
InversePose | import torch
import torch.nn as nn
def inverse_pose(pose, eps=1e-06):
"""Function that inverts a 4x4 pose.
Args:
points (Tensor): tensor with poses.
Returns:
Tensor: tensor with inverted poses.
Shape:
- Input: :math:`(N, 4, 4)`
- Output: :math:`(N, 4, 4)`
Exampl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Wizaron/torchgeometry | InversePose | false | 5,987 | [
"Apache-2.0"
] | 1 | 59a8d25dd811ded6a139d5c0c2442b06f43dc775 | https://github.com/Wizaron/torchgeometry/tree/59a8d25dd811ded6a139d5c0c2442b06f43dc775 |
L1_Charbonnier_loss | import torch
import torch.utils.data
from torch.nn.modules.loss import _Loss
class L1_Charbonnier_loss(_Loss):
"""
L1 Charbonnierloss
"""
def __init__(self, para):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 0.001
def forward(self, X, Y):
diff = torch.add(X, -Y... | 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... | YDDDDG/3D2Unet | L1_Charbonnier_loss | false | 6,002 | [
"MIT"
] | 1 | daca056958fb2ae319dc18a350e04b3cefe0d99f | https://github.com/YDDDDG/3D2Unet/tree/daca056958fb2ae319dc18a350e04b3cefe0d99f |
kernelPredictor | import torch
import torch.nn as nn
class kernelPredictor(nn.Module):
def __init__(self, in_ch, hid_ch, pred_kernel_size=21):
super(kernelPredictor, self).__init__()
self.act = nn.ReLU()
self.conv1 = nn.Conv2d(in_ch, hid_ch, kernel_size=1)
self.conv2 = nn.Conv2d(hid_ch, pred_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
assert_... | qbhan/pathembed | kernelPredictor | false | 7,515 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
SubjObjSpan | import torch
import numpy as np
from typing import Iterable
from typing import Optional
import torch.nn as nn
def find_closest_span_pairs(head: 'Iterable', tail: 'Iterable', backtrace:
'Optional[bool]'=True):
"""
Find all span pairs.
Args:
head: list of start position predictions, either 1 or... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from typing import Iterable
from typing import Optional
impor... | Spico197/REx | SubjObjSpan | false | 17,945 | [
"MIT"
] | 4 | bb3cdb845765a63e9bd18070068af52a1b2db3f3 | https://github.com/Spico197/REx/tree/bb3cdb845765a63e9bd18070068af52a1b2db3f3 |
my_BinaryCross | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | carsault/chord_sequence_prediction | my_BinaryCross | false | 1,679 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 |
FeatLoss | # 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... | CityU-AIM-Group/SIGMA | FeatLoss | false | 17,408 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 |
MaxOut | import torch
import torch.nn as nn
class MaxOut(nn.Module):
def __init__(self, pool_size):
super(MaxOut, self).__init__()
self.pool_size = pool_size
def forward(self, ipt):
"""
input:
reduce_size:
"""
input_size = list(ipt.size())
assert input_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions | MaxOut | false | 14,698 | [
"MIT"
] | 62 | 6e5ef241c64b5b30a6ff54ddad31e610013b8388 | https://github.com/YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions/tree/6e5ef241c64b5b30a6ff54ddad31e610013b8388 |
Multi_Head_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Moon-xm/Chinese-Text-Classification-Pytorch | Multi_Head_Attention | false | 11,725 | [
"MIT"
] | 0 | 19fe64006418bf4296f884e4d1f038c17b34d3de | https://github.com/Moon-xm/Chinese-Text-Classification-Pytorch/tree/19fe64006418bf4296f884e4d1f038c17b34d3de |
Sum | import torch
import torch.nn as nn
import torch.utils.data
class Sum(nn.Module):
def __init__(self, n, weight=False):
super(Sum, self).__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=Tru... | 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.... | ChaokunChang/SVAS | Sum | false | 254 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
SSE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ColinWine/Accurate-and-rapid-pulmonary-tuberculosis-diagnosis-system | SSE | false | 303 | [
"Apache-2.0"
] | 0 | 7be433b3a495a7c4db2b850a79dc505e413909c4 | https://github.com/ColinWine/Accurate-and-rapid-pulmonary-tuberculosis-diagnosis-system/tree/7be433b3a495a7c4db2b850a79dc505e413909c4 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Lambda(nn.Module):
"""An easy way to create a pytorch layer for a simple `func`."""
def __init__(self, func):
"""create a layer that simply calls `func` with `x`"""
super().__init__()
self.func = func
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | eminem171333491/PaddleOCR2Pytorch | Encoder | false | 3,471 | [
"Apache-2.0"
] | 0 | ec466bb3a689eccb9290e9f80812a45301d3b030 | https://github.com/eminem171333491/PaddleOCR2Pytorch/tree/ec466bb3a689eccb9290e9f80812a45301d3b030 |
GaussianNoise | import torch
import torch.nn as nn
class GaussianNoise(nn.Module):
"""A gaussian noise module.
Args:
stddev (float): The standard deviation of the normal distribution.
Default: 0.1.
Shape:
- Input: (batch, *)
- Output: (batch, *) (same shape as input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | eezkni/UEGAN | GaussianNoise | false | 15,285 | [
"MIT"
] | 73 | a6616ac559819d487cae0f301d98cf2922a11a09 | https://github.com/eezkni/UEGAN/tree/a6616ac559819d487cae0f301d98cf2922a11a09 |
SeperableConv | # 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_... | AksultanMukhanbet/proctoring_intellectual_part | SeperableConv | false | 8,831 | [
"MIT"
] | 0 | f85db9d31025cb57a732f64ab22358651bc93c69 | https://github.com/AksultanMukhanbet/proctoring_intellectual_part/tree/f85db9d31025cb57a732f64ab22358651bc93c69 |
HeatmapLoss | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing
class HeatmapLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt, mask):
assert pred.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
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.m... | chaowentao/DEKRv2 | HeatmapLoss | false | 3,280 | [
"MIT"
] | 0 | e092c3eb10766b099a8a9681dc26f9eb781ec070 | https://github.com/chaowentao/DEKRv2/tree/e092c3eb10766b099a8a9681dc26f9eb781ec070 |
MultiHeadAttention | import math
import torch
import torch.nn.functional as F
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | HebatallaTarek/Empathy-Mental-Health | MultiHeadAttention | false | 13,782 | [
"BSD-3-Clause"
] | 66 | 16e2a5f93aabd22803bb39805f8e76c8bea0ccf2 | https://github.com/HebatallaTarek/Empathy-Mental-Health/tree/16e2a5f93aabd22803bb39805f8e76c8bea0ccf2 |
RNNAgent | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_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
import torch.nn as nn
assert_... | johnson7788/pymarl2 | RNNAgent | false | 3,907 | [
"Apache-2.0"
] | 0 | 8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 | https://github.com/johnson7788/pymarl2/tree/8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 |
DownSample | import torch
import torch.nn as M
def DepthwiseConv(in_channels, kernel_size, stride, padding):
return M.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=
in_channels, bias=False)
def PointwiseConv(in_channels, out_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 M
assert_s... | SuperbTUM/RAW-image-denoising | DownSample | false | 17,973 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab |
L1Loss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ChHanXiao/mmdetection | L1Loss | false | 9,145 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
ycbcr_to_rgb_jpeg | # 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... | DazhiZhong/DiffJPEG | ycbcr_to_rgb_jpeg | false | 9,001 | [
"MIT"
] | 0 | e20de92539f31a57906ae4c32a41dc46e774c316 | https://github.com/DazhiZhong/DiffJPEG/tree/e20de92539f31a57906ae4c32a41dc46e774c316 |
SegmentalTransformerEncoder | # 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.... | cmdowney88/XLSLM | SegmentalTransformerEncoder | false | 3,302 | [
"MIT"
] | 0 | 7fe266bd0f0ad8a79a30052a18104b974d1c32e8 | https://github.com/cmdowney88/XLSLM/tree/7fe266bd0f0ad8a79a30052a18104b974d1c32e8 |
HardSigmoid | import torch
import torch.nn as nn
class HardSigmoid(nn.Module):
def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0):
super(HardSigmoid, self).__init__()
assert divisor != 0, 'divisor is not allowed to be equal to zero'
self.bias = bias
self.divisor = divisor
... | 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... | DetectionBLWX/WSDDN.pytorch | HardSigmoid | false | 17,216 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 |
MSEloss_mod | # 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... | DemainWang/TP2Net | MSEloss_mod | false | 11,337 | [
"MIT"
] | 0 | ebdd509ac674c107de59062382a9f9d59f86b492 | https://github.com/DemainWang/TP2Net/tree/ebdd509ac674c107de59062382a9f9d59f86b492 |
TSA_Fusion | # 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_... | CM-BF/FeatureFlow | TSA_Fusion | false | 13,550 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
ScaledDotProductAttention | import torch
def masked_softmax(vector: 'torch.Tensor', mask: 'torch.Tensor', dim: 'int'
=-1, memory_efficient: 'bool'=False, mask_fill_value: 'float'=-1e+32
) ->torch.Tensor:
"""
https://github.com/allenai/allennlp/blob/b6cc9d39651273e8ec2a7e334908ffa9de5c2026/allennlp/nn/util.py#L231
``torch.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.... | IouJenLiu/AFK | ScaledDotProductAttention | false | 5,347 | [
"MIT"
] | 1 | db2b47bb3a5614b61766114b87f143e4a61a4a8d | https://github.com/IouJenLiu/AFK/tree/db2b47bb3a5614b61766114b87f143e4a61a4a8d |
SiamFC | import torch
import torch.nn as nn
import torch.nn.functional as F
class SiamFC(nn.Module):
def __init__(self, out_scale=0.001):
super(SiamFC, self).__init__()
self.out_scale = out_scale
def forward(self, z, x):
return self._fast_xcorr(z, x) * self.out_scale
def _fast_xcorr(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | LIANGKE23/Siamese-FC-KF-CF | SiamFC | false | 17,608 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f |
DenseCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(log... | 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
... | LichenYang-Jeffrey/DCL-with-Efficient-B7 | DenseCrossEntropy | false | 17,569 | [
"MIT"
] | 4 | 84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e | https://github.com/LichenYang-Jeffrey/DCL-with-Efficient-B7/tree/84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e |
KeyValueAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init
class KeyValueAttention(nn.Module):
def __init__(self, query_size, key_size, value_size, hid_size, init_range):
super(KeyValueAttention, self).__init__()
self.key2hid = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | haojiepan1/CrossWOZ | KeyValueAttention | false | 6,811 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
StyleEmbedAttention | # 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.... | ishine/Comprehensive-Transformer-TTS | StyleEmbedAttention | false | 15,622 | [
"MIT"
] | 147 | dca252cae50a18464ce2410aa85a21c557c72d7a | https://github.com/ishine/Comprehensive-Transformer-TTS/tree/dca252cae50a18464ce2410aa85a21c557c72d7a |
RMSELoss | import torch
class RMSELoss(torch.nn.Module):
def __init__(self, eps=1e-08):
super(RMSELoss, self).__init__()
self.eps = eps
self.criterion = torch.nn.MSELoss()
def forward(self, y_hat, y):
return torch.sqrt(self.criterion(y_hat, y) + self.eps)
def get_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | cvpr22sub7201/SpeechDrivenTongueAnimation | RMSELoss | false | 6,500 | [
"MIT"
] | 1 | 82caf9d7f4331e039e3b2f0d31df6393d24ccb1c | https://github.com/cvpr22sub7201/SpeechDrivenTongueAnimation/tree/82caf9d7f4331e039e3b2f0d31df6393d24ccb1c |
L2Norm | import torch
class L2Norm(torch.nn.Module):
def __init__(self):
super(L2Norm, self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sqrt(torch.sum(x * x, dim=1) + self.eps)
if len(norm.size()) == 1:
x = x / norm.unsqueeze(-1).expand_as(x)
el... | 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... | RiyaoDong/HGSL | L2Norm | false | 2,778 | [
"Apache-2.0"
] | 0 | 19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 | https://github.com/RiyaoDong/HGSL/tree/19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | manuelhz/dissertation | DiceLoss | false | 3,972 | [
"MIT"
] | 0 | ca89475f79505dfb6d8a3645ca85451df7fce3b6 | https://github.com/manuelhz/dissertation/tree/ca89475f79505dfb6d8a3645ca85451df7fce3b6 |
DepthWiseSeparableConvBlock | import torch
import torch.nn as nn
class DepthWiseSeparableConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, bias=True, padding_mode='zeros',
inner_kernel_size=1, inner_stride=1, inner_padding=0):
"""Depthwise separable 2D Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | pppyykknen/LFDisplay-PyTorch | DepthWiseSeparableConvBlock | false | 4,135 | [
"MIT"
] | 0 | d19261dac1717a799bb5ba5f96563be1d2383340 | https://github.com/pppyykknen/LFDisplay-PyTorch/tree/d19261dac1717a799bb5ba5f96563be1d2383340 |
Wav2Vec2ClassificationHead | # 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_... | HLasse/wav2vec_finetune | Wav2Vec2ClassificationHead | false | 18,359 | [
"MIT"
] | 6 | 084ab432ba4acbf5ce81267e2791fb36a0b70daa | https://github.com/HLasse/wav2vec_finetune/tree/084ab432ba4acbf5ce81267e2791fb36a0b70daa |
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 import triton_helpers
import torch.nn as nn
assert_... | HuangCongQing/pytorch | ResidualBlock | false | 8,231 | [
"MIT"
] | 12 | 2b2b01d74b45cbe4e467da229798609e79cec97c | https://github.com/HuangCongQing/pytorch/tree/2b2b01d74b45cbe4e467da229798609e79cec97c |
SNNBlock | from torch.nn import Module
import math
import torch
from torch.nn import SELU
from torch.nn import AlphaDropout
from torch.nn import Identity
from torch.nn import Parameter
from torch.nn.functional import conv2d
class SNNBlock(Module):
"""Block for a self-normalizing fully-connected layer.
This block consis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | rharish101/CIL-Project | SNNBlock | false | 4,186 | [
"MIT"
] | 0 | fed1be8b22bb4228329b719a301f74459a7bf13b | https://github.com/rharish101/CIL-Project/tree/fed1be8b22bb4228329b719a301f74459a7bf13b |
NTM | from _paritybench_helpers import _mock_config
import logging
import torch
import numpy as np
from torch.nn import functional as F
import torch.utils.data
import torch.nn as nn
class NTM(nn.Module):
def __init__(self, opt, hidden_dim=500, l1_strength=0.001):
super(NTM, self).__init__()
self.input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Nullius-2020/TAKG-Paddle | NTM | false | 14,423 | [
"MIT"
] | 130 | 7ebb5c4cdd1d2c68b1ca4a518b73c5e815fc5812 | https://github.com/Nullius-2020/TAKG-Paddle/tree/7ebb5c4cdd1d2c68b1ca4a518b73c5e815fc5812 |
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.... | Moymix/BERT-pytorch | Attention | false | 5,599 | [
"Apache-2.0"
] | 1 | f0b9c3ae53e05c00adcc761e0422e4222d8b5619 | https://github.com/Moymix/BERT-pytorch/tree/f0b9c3ae53e05c00adcc761e0422e4222d8b5619 |
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 ... | ljjyxz123/CenterMask | SpatialAttention | false | 7,112 | [
"BSD-2-Clause"
] | 1 | 443eebde30e209eeb3b953f7ef35d3f7f14aaca5 | https://github.com/ljjyxz123/CenterMask/tree/443eebde30e209eeb3b953f7ef35d3f7f14aaca5 |
DeepContinuor | # 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_... | simonverret/deep_continuation | DeepContinuor | false | 4,346 | [
"MIT"
] | 0 | 986bfba7f6806dc4869a023ff1fc1d0d18324b25 | https://github.com/simonverret/deep_continuation/tree/986bfba7f6806dc4869a023ff1fc1d0d18324b25 |
RobertaHierarchyHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | abrinkmann/productCategorization | RobertaHierarchyHead | false | 18,216 | [
"MIT"
] | 5 | 75732e4b1c9da941a793db80b5fe2245bae45e87 | https://github.com/abrinkmann/productCategorization/tree/75732e4b1c9da941a793db80b5fe2245bae45e87 |
MDNLayer | import torch
from torch import nn
from torch.nn import functional as F
class MDNLayer(nn.Module):
""" Mixture Density Network layer
The input maps to the parameters of a Mixture of Gaussians (MoG) probability
distribution, where each Gaussian has out_dim dimensions and diagonal covariance.
If dim_wis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | oatsu-gh/nnsvs | MDNLayer | false | 16,206 | [
"MIT"
] | 298 | 510f37bc1d1f15282646e4d34435b5d63686cf40 | https://github.com/oatsu-gh/nnsvs/tree/510f37bc1d1f15282646e4d34435b5d63686cf40 |
ACELoss | import torch
import torch.nn as nn
class ACELoss(nn.Module):
"""
Ref: [1] Aggregation Cross-Entropy for Sequence Recognition. CVPR-2019
"""
def __init__(self, character, eps=1e-10):
"""
Args:
character (dict): recognition dictionary
eps (float): margin of erro... | 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
... | hikopensource/DAVAR-Lab-OCR | ACELoss | false | 15,509 | [
"Apache-2.0"
] | 387 | c65285f6668864cca7a12770ae4c8d083ea1cf1b | https://github.com/hikopensource/DAVAR-Lab-OCR/tree/c65285f6668864cca7a12770ae4c8d083ea1cf1b |
L2loss | # 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... | Elameri/ivadomed | L2loss | false | 9,295 | [
"MIT"
] | 0 | 76b5cea46f90f938aafd5ec26e072d559c764b43 | https://github.com/Elameri/ivadomed/tree/76b5cea46f90f938aafd5ec26e072d559c764b43 |
ExternalAttention | # 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.... | LiChengChen666/DetectDee | ExternalAttention | false | 9,814 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | zzz123xyz/pytorch-3dunet | DiceLoss | false | 11,039 | [
"MIT"
] | 0 | 5bab6968b23cff5c6ae456b343285bfa9b104294 | https://github.com/zzz123xyz/pytorch-3dunet/tree/5bab6968b23cff5c6ae456b343285bfa9b104294 |
Net | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Dropout2d
from torch.nn import Linear
from torch.nn.functional import relu
from torch.nn.functional import max_pool2d
from torch.nn.functional import log_softmax
from torch import flatten
class Net(Module):
def __init__(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AhmetTavli/Olivetti-CNN | Net | false | 11,237 | [
"MIT"
] | 0 | 174747382f17e02c0e5f964d08a449429ac6fbd8 | https://github.com/AhmetTavli/Olivetti-CNN/tree/174747382f17e02c0e5f964d08a449429ac6fbd8 |
Similarity | # 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.... | zhu-y11/multilingual_treelstm | Similarity | false | 13,180 | [
"MIT"
] | 0 | 39c211f3c03db733f776aa8fe73cd615aaa47465 | https://github.com/zhu-y11/multilingual_treelstm/tree/39c211f3c03db733f776aa8fe73cd615aaa47465 |
ResidualSequential | import torch
import torch.optim
import torch.nn as nn
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None
if o... | 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.optim
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | ChongYou/robust-image-recovery | ResidualSequential | false | 7,881 | [
"MIT"
] | 13 | 5bb23142509f307d31fd435de12787a70ec3a5bc | https://github.com/ChongYou/robust-image-recovery/tree/5bb23142509f307d31fd435de12787a70ec3a5bc |
MLP | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, hidden_dim, out_dim=None) ->None:
super().__init__()
out_dim = out_dim or dim
self.fc1 = nn.Conv2d(dim, hidden_dim, 1, 1, 0)
self.act = nn.ReLU6(True)
self.fc2 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | sithu31296/image_classification | MLP | false | 16,459 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
PatchMerging | # 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 ... | billpsomas/ibot | PatchMerging | false | 1,553 | [
"Apache-2.0"
] | 0 | c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d | https://github.com/billpsomas/ibot/tree/c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d |
SimpleModel | # 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.... | bratao/DeepSpeed | SimpleModel | false | 6,359 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
RegLoss | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def _reg_loss(regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objec... | 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
... | XiangLiK/cv_course | RegLoss | false | 18,114 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 |
Discriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XrosLiang/GraphCL | Discriminator | false | 6,003 | [
"MIT"
] | 1 | fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c | https://github.com/XrosLiang/GraphCL/tree/fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c |
GCNSynthetic | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(Grap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Armagaan/cf-gnnexplainer | GCNSynthetic | false | 7,735 | [
"MIT"
] | 15 | 22b415e114c52d8d60ca45a40c3cb33c1947400c | https://github.com/Armagaan/cf-gnnexplainer/tree/22b415e114c52d8d60ca45a40c3cb33c1947400c |
TorchDiv | # 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... | bunderhi/torch2trt | TorchDiv | false | 1,612 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
FocusLayer | import torch
import torch.nn as nn
class FocusLayer(nn.Module):
def __init__(self, c1, c2, k=1):
super(FocusLayer, self).__init__()
def forward(self, x):
return torch.cat([x[..., ::2], x[..., 1::2]], dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | OrigamiSL/TCCT2021-Neurocomputing- | FocusLayer | false | 17,777 | [
"Apache-2.0"
] | 4 | c98c7add5d68510db61f49038970d145393d42a5 | https://github.com/OrigamiSL/TCCT2021-Neurocomputing-/tree/c98c7add5d68510db61f49038970d145393d42a5 |
NormalizeImages | import torch
import torch.nn as nn
class NormalizeImages(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
flat = x.view(x.size(0), -1)
mp = torch.mean(flat, dim=1)
sp = torch.std(flat, dim=1) + 1e-07
return (x - mp.detach().unsqueeze(-1).unsque... | 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_... | matteo-ronchetti/IKA | NormalizeImages | false | 7,178 | [
"MIT"
] | 1 | 29d1752a059c3ab7659b332b72bf8c1506e7dd20 | https://github.com/matteo-ronchetti/IKA/tree/29d1752a059c3ab7659b332b72bf8c1506e7dd20 |
ResNNFlow | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | nicola-decao/M-NAF-experiments-VAE | ResNNFlow | false | 4,086 | [
"MIT"
] | 0 | b8e127205e84d94ae50618e95734f20d259f7934 | https://github.com/nicola-decao/M-NAF-experiments-VAE/tree/b8e127205e84d94ae50618e95734f20d259f7934 |
SigmoidFocalClassificationLoss | # 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... | ElodieShan/OpenPCDet | SigmoidFocalClassificationLoss | false | 9,030 | [
"Apache-2.0"
] | 0 | d23959d70c73b29f3f14462628fa8520a64f2eae | https://github.com/ElodieShan/OpenPCDet/tree/d23959d70c73b29f3f14462628fa8520a64f2eae |
BuildBlock | # 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.functional as... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | BuildBlock | false | 14,852 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
BinaryReg | import torch
import torch.nn as nn
import torch.utils.data
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
def forward(self, input):
diff = input - 0.5
dif... | 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
... | pragyasingh7/pytorch_connectomics | BinaryReg | false | 4,134 | [
"MIT"
] | 0 | fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 | https://github.com/pragyasingh7/pytorch_connectomics/tree/fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 |
Pooling | import torch
import torch.nn as nn
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size //
2, count_incl... | 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... | TranNhiem/MVAR_SSL | Pooling | false | 5,910 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
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.... | Jeffyrao/translate | MultiheadAttention | false | 2,422 | [
"BSD-3-Clause"
] | 0 | ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 | https://github.com/Jeffyrao/translate/tree/ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 |
ExpModule | import torch
import torch.nn as nn
class ExpModule(nn.Module):
def __init__(self):
super(ExpModule, self).__init__()
def forward(self, x):
return torch.exp(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | SimonTreu/sdvae | ExpModule | false | 1,096 | [
"MIT"
] | 0 | e0270b9b2acf2d66eec93870f1c5633c8f04d9ab | https://github.com/SimonTreu/sdvae/tree/e0270b9b2acf2d66eec93870f1c5633c8f04d9ab |
PointLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride... | DreamBlack/APCNet | PointLoss | false | 385 | [
"MIT"
] | 0 | d76bc9e46c3b631035c5c67e2367b6fb80621333 | https://github.com/DreamBlack/APCNet/tree/d76bc9e46c3b631035c5c67e2367b6fb80621333 |
KLDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SebyakinAndrei/MichiGAN | KLDLoss | false | 1,047 | [
"MIT"
] | 0 | 6584c9a106b33096f38e8f5b11d0320f7065fd26 | https://github.com/SebyakinAndrei/MichiGAN/tree/6584c9a106b33096f38e8f5b11d0320f7065fd26 |
Clamp | import torch
import torch.optim
class Clamp(torch.nn.Module):
min_value: 'float'
max_value: 'float'
def __init__(self, min_value: 'float'=0.0, max_value: 'float'=1.0):
super(Clamp, self).__init__()
self.min_value = min_value
self.max_value = max_value
def forward(self, x: 'to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | ai-in-motion/moai | Clamp | false | 18,321 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
QNet | # 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
from to... | ven-kyoshiro/PILCO-1 | QNet | false | 10,966 | [
"MIT"
] | 0 | 61c4ef18a6bbecbeb6a10784a7925d31f46dd23b | https://github.com/ven-kyoshiro/PILCO-1/tree/61c4ef18a6bbecbeb6a10784a7925d31f46dd23b |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DocYard-ai/UCR | EncoderLayer | false | 8,026 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
_GLUBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class _GLUBlock(nn.Module):
def __init__(self, n_c_in, n_c_out):
super(_GLUBlock, self).__init__()
self.pad = nn.ConstantPad1d((1, 2), 0)
self.conv_data = nn.Conv1d(n_c_in, n_c_out, 4, stride=1, bias=True)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | KaibinBao/neuralnilm-pytorch | _GLUBlock | false | 17,530 | [
"Apache-2.0"
] | 4 | 017b85fc921f0638f93a0e16f615028f60b7d279 | https://github.com/KaibinBao/neuralnilm-pytorch/tree/017b85fc921f0638f93a0e16f615028f60b7d279 |
AffineConstantFlow | import torch
from torch import nn
class AffineConstantFlow(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super().__... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | JannerM/gamma-models | AffineConstantFlow | false | 8,329 | [
"MIT"
] | 32 | 4b40d828bf228385c3081d359cdc3494d70de4a1 | https://github.com/JannerM/gamma-models/tree/4b40d828bf228385c3081d359cdc3494d70de4a1 |
tofp16 | # 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... | AnonymousAuthors444/VEC_VAD | tofp16 | false | 13,240 | [
"MIT"
] | 67 | 0072bf857030e621e2f9c12689407b81e45ed603 | https://github.com/AnonymousAuthors444/VEC_VAD/tree/0072bf857030e621e2f9c12689407b81e45ed603 |
LogSoftMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | mshmoon/siamrpn-lightweight | LogSoftMax | false | 7,291 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
MAELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | anglixjtu/MSG_CHN_WACV20 | MAELoss | false | 14,841 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
Keypoint3DLoss | # 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... | michael-p-sachen/ProHMR | Keypoint3DLoss | false | 10,575 | [
"BSD-3-Clause"
] | 0 | 0167d05a9a45939a217d02b4ef8fd67977c15f82 | https://github.com/michael-p-sachen/ProHMR/tree/0167d05a9a45939a217d02b4ef8fd67977c15f82 |
RPNHead | import torch
import torch.nn.functional as F
from torch import nn
class RPNHead(nn.Module):
def __init__(self, in_channels, num_anchors):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, 1)
self.bbox_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | yokosyun/instance-segmentation | RPNHead | false | 4,628 | [
"MIT"
] | 0 | 5779ae864b24c28300b0ddc4c314e63490215606 | https://github.com/yokosyun/instance-segmentation/tree/5779ae864b24c28300b0ddc4c314e63490215606 |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AutuanLiu/LeetCode2019 | ScaledDotProductAttention | false | 4,875 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 |
CriticNet | # 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.... | geektoni/AlphaNPI | CriticNet | false | 3,534 | [
"MIT"
] | 0 | ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 | https://github.com/geektoni/AlphaNPI/tree/ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 |
CXLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class CXLoss(nn.Module):
def __init__(self, sigma=0.1, b=1.0, similarity='consine'):
super(CXLoss, self).__init__()
self.similarity = similarity
self.sigma = sigma
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.... | drgripa1/deepvecfont | CXLoss | false | 15,263 | [
"MIT"
] | 68 | a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | https://github.com/drgripa1/deepvecfont/tree/a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 |
MultiheadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, attn_dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
"""
Multi-headed attention. This module can... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | SCUT-IEL/CMAA | MultiheadAttention | false | 11,845 | [
"MIT"
] | 0 | 1af9e7a7a75e754a7208e361d8128ef58b716941 | https://github.com/SCUT-IEL/CMAA/tree/1af9e7a7a75e754a7208e361d8128ef58b716941 |
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