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 |
|---|---|---|---|---|---|---|---|---|---|---|
LayerScaling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LayerScaling(nn.Module):
"""Scales inputs by the root of the second moment for groups of channels.
.. math::
y_g = \\frac{x_g}{\\sqrt{\\mathrm{E}[x_g^2]... | 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.nn.parallel
import torch.optim
import torch.... | ClashLuke/online-normalization | LayerScaling | false | 13,515 | [
"BSD-3-Clause"
] | 55 | fe08b9f8e288d628eee4f9991e562cdb4f9e997b | https://github.com/ClashLuke/online-normalization/tree/fe08b9f8e288d628eee4f9991e562cdb4f9e997b |
MLP_model | import torch
import torch.nn as nn
class MLP_model(nn.Module):
def __init__(self, inputsize, layer1, layer2, layer3, device):
super().__init__()
self.fc1 = nn.Linear(inputsize, layer1)
self.fc2 = nn.Linear(layer1, layer2)
self.fc3 = nn.Linear(layer2, layer3)
self.fc4 = 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
import torch.nn as nn
assert_... | GYMS-PKU/HIgh-Frequency-Predictor | MLP_model | false | 5,182 | [
"Apache-2.0"
] | 1 | aac5efa73d6e15d95d1b99d529dcf639fb8181f4 | https://github.com/GYMS-PKU/HIgh-Frequency-Predictor/tree/aac5efa73d6e15d95d1b99d529dcf639fb8181f4 |
RingLoss | import torch
import warnings
import torch.nn as nn
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self):
super(RingLoss, self).__init__()
warnings.warn('This method is ... | 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 warnings
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gua... | ArronHZG/ABD-Net | RingLoss | false | 9,588 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
KdMseLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KdMseLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits_S, logits_T, temperature=1):
"""
Calculate the mse loss between logits_S and logits_T
:param logits_S: Tensor of sha... | 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... | lonePatient/TorchBlocks | KdMseLoss | false | 15,959 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | WeisiX/ITAS3D | L1 | false | 18,108 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 |
EQ | # 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... | Ilyabasharov/torch2trt | EQ | false | 2,522 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ImgPatchConverter | # 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... | Asichurter/MalFusionFSL | ImgPatchConverter | false | 16,977 | [
"MIT"
] | 4 | 713bf64cc07a3489f42941fd2299837075575ac0 | https://github.com/Asichurter/MalFusionFSL/tree/713bf64cc07a3489f42941fd2299837075575ac0 |
LeNet | # 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... | ducnguyenhuynh/via-trafficsign-classification | LeNet | false | 6,631 | [
"MIT"
] | 1 | e65fccc1ee377603334453eacfc3f65619dc0714 | https://github.com/ducnguyenhuynh/via-trafficsign-classification/tree/e65fccc1ee377603334453eacfc3f65619dc0714 |
Theta | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
from typing import Optional
from typing impo... | mstoelzle/Transfer-Learning-Library | Theta | false | 12,877 | [
"MIT"
] | 0 | 7d5022668cbe6d1bedbc7c386d44b9d89c272d6b | https://github.com/mstoelzle/Transfer-Learning-Library/tree/7d5022668cbe6d1bedbc7c386d44b9d89c272d6b |
LinearModel | # 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... | billpsomas/incremental_learning.pytorch | LinearModel | false | 14,961 | [
"MIT"
] | 277 | a401a6609fc61c74698739cf937c0ece1c10913f | https://github.com/billpsomas/incremental_learning.pytorch/tree/a401a6609fc61c74698739cf937c0ece1c10913f |
ComplexConv | import torch
import torch.nn as nn
class ComplexConv(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(ComplexConv, self).__init__()
self.device = torch.device('cuda' if torch.cuda.is_available() else
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | iseeklin/Electromagnetic-Signal-Recognition-Using-Deep-Learning | ComplexConv | false | 10,211 | [
"Apache-2.0"
] | 0 | be78a2d966f33fd90567b21295cda1c1d472e14a | https://github.com/iseeklin/Electromagnetic-Signal-Recognition-Using-Deep-Learning/tree/be78a2d966f33fd90567b21295cda1c1d472e14a |
Conv1dKeepLength | # 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.utils.... | Ninushkat/Impact-Synth-Hardware | Conv1dKeepLength | false | 14,111 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 |
SoftCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | KateHaeun/pycls | SoftCrossEntropyLoss | false | 11,602 | [
"MIT"
] | 0 | f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | https://github.com/KateHaeun/pycls/tree/f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 |
ELUPlus | import torch
from torch import nn
import torch.nn
class ELUPlus(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
def forward(self, x):
return self.elu(x) + 1.0
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 libdevice
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | MilesCranmer/nflows | ELUPlus | false | 11,703 | [
"MIT"
] | 0 | 6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 | https://github.com/MilesCranmer/nflows/tree/6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 |
SmallVDSR_16x | # 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_... | MingSun-Tse/pytorch-vdsr | SmallVDSR_16x | false | 5,601 | [
"MIT"
] | 1 | 597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 | https://github.com/MingSun-Tse/pytorch-vdsr/tree/597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 |
BCEWithLogitsLoss2d | # 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... | MIPT-Oulu/Collagen | BCEWithLogitsLoss2d | false | 17,656 | [
"MIT"
] | 4 | 0cbc4285d60e5c9fcc89f629fcf4321e80b7452c | https://github.com/MIPT-Oulu/Collagen/tree/0cbc4285d60e5c9fcc89f629fcf4321e80b7452c |
Environment | import torch
import torch.nn.functional as F
import torch.nn as nn
class Environment(nn.Module):
"""Defines the Environment module as an ANN"""
def __init__(self, state_size: 'int', action_size: 'int', fc1: 'int'=24,
fc2: 'int'=24):
super(Environment, self).__init__()
self.state_size ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | QasimWani/EARL | Environment | false | 953 | [
"MIT"
] | 0 | 05c303335e67903380771c4954a5317bd46fc0e7 | https://github.com/QasimWani/EARL/tree/05c303335e67903380771c4954a5317bd46fc0e7 |
Net1 | import torch
import torch.nn.functional as F
from torch import nn
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(64,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DPBayes/DP-cross-silo-federated-learning | Net1 | false | 17,207 | [
"Apache-2.0"
] | 8 | 6707db703de5fae48c06116ae8ceee0685c9615d | https://github.com/DPBayes/DP-cross-silo-federated-learning/tree/6707db703de5fae48c06116ae8ceee0685c9615d |
LosslessRGB | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.utils.data
from torch import nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_s... | KazutakaYamanouchi/bachelor-study | LosslessRGB | false | 2,618 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
MinusOne | import torch
import torch.optim
class MinusOne(torch.nn.Module):
def __init__(self):
super(MinusOne, self).__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x - 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | MinusOne | false | 18,315 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
PredictionConvolutions | import torch
from torch import nn
from itertools import product as product
import torch.optim
import torch.utils.data
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from itertools import product as product
import torch.optim... | gigajet/ICDAR-2019-SROIE | PredictionConvolutions | false | 12,768 | [
"MIT"
] | 0 | 62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 | https://github.com/gigajet/ICDAR-2019-SROIE/tree/62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 |
ABS_disc | # 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... | Sampson-Lee/SIB-Net | ABS_disc | false | 2,798 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
Mlp | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Mlp(nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy as np
... | bubbliiiing/classification-pytorch | Mlp | false | 14,988 | [
"MIT"
] | 88 | ee62c05bd3094c3fab48bada5a57cb2ed8b61c11 | https://github.com/bubbliiiing/classification-pytorch/tree/ee62c05bd3094c3fab48bada5a57cb2ed8b61c11 |
UpsamplingBilinear | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class UpsamplingBilinear(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = 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 import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | UpsamplingBilinear | false | 17,969 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 |
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
from torch import n... | fukuroder/pytorch_lightning_wavenet | ResidualBlock | false | 6,714 | [
"MIT"
] | 1 | 440ef4092397998edf0df4625f1f10157db2243e | https://github.com/fukuroder/pytorch_lightning_wavenet/tree/440ef4092397998edf0df4625f1f10157db2243e |
NeuralModel | # 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.... | sumitsharmamanit/Facial-emotion-recognition | NeuralModel | false | 10,806 | [
"Apache-2.0"
] | 0 | f95770c0cfabd46a8f9589eb415ce69eaeaea4c6 | https://github.com/sumitsharmamanit/Facial-emotion-recognition/tree/f95770c0cfabd46a8f9589eb415ce69eaeaea4c6 |
XSigmoidLoss | import torch
import torch.nn as nn
class XSigmoidLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(2 * ey_t / (1 + torch.exp(-ey_t)) - ey_t)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), tor... | 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
... | IshanBaliyan/DEEP-TFM_with_cGAN | XSigmoidLoss | false | 5,349 | [
"MIT"
] | 1 | 8d711c025367031197e5b8c7c768fc9fbea406ce | https://github.com/IshanBaliyan/DEEP-TFM_with_cGAN/tree/8d711c025367031197e5b8c7c768fc9fbea406ce |
MatrixAdd | import torch
import torch.nn as nn
import torch.autograd
class MatrixAdd(nn.Module):
def __init__(self):
super(MatrixAdd, self).__init__()
def forward(self, x, y):
z = torch.add(x, y)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._d... | hirayamy/nngen | MatrixAdd | false | 12,501 | [
"Apache-2.0"
] | 0 | 63f72be83e4bb1a697a969fb6a14d0335ec0316f | https://github.com/hirayamy/nngen/tree/63f72be83e4bb1a697a969fb6a14d0335ec0316f |
ChannelPool | import torch
import torch.nn as nn
import torch.utils.model_zoo
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | HolmesShuan/OISR-PyTorch | ChannelPool | false | 13,770 | [
"BSD-2-Clause"
] | 141 | bbe0c88f71fe565a2842df7971b62a9bc5a56c48 | https://github.com/HolmesShuan/OISR-PyTorch/tree/bbe0c88f71fe565a2842df7971b62a9bc5a56c48 |
AcM | import torch
from torch import nn
class AcM(nn.Module):
def __init__(self, in_dim: 'int', ac_dim: 'int', ac_lim: 'int',
discrete: 'bool'=True):
super().__init__()
self.ac_lim = ac_lim
self.discrete = discrete
self.fc1 = nn.Linear(in_dim, 64)
self.fc2 = nn.Linear(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MIMUW-RL/spp-rl | AcM | false | 17,644 | [
"MIT"
] | 7 | 86b96cdd220cc4eae86f7cfd26924c69b498dcc6 | https://github.com/MIMUW-RL/spp-rl/tree/86b96cdd220cc4eae86f7cfd26924c69b498dcc6 |
GenerativeLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class GenerativeLoss(nn.Module):
def __init__(self):
super(GenerativeLoss, self).__init__()
self.criterion = nn.BCELoss(reduction='mean')
def forward(sel... | 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... | SkywalkerAtlas/HRGAN | GenerativeLoss | false | 5,836 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
SFU | # 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 ... | hansd410/mnemonic | SFU | false | 3,577 | [
"BSD-3-Clause"
] | 0 | 409508d08da7f5d5940ffb56fd9715e6ef1e68a3 | https://github.com/hansd410/mnemonic/tree/409508d08da7f5d5940ffb56fd9715e6ef1e68a3 |
BasicModel4_MultiArgs | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel4_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)
"""
def __init__(self) ->None:
super().__in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel4_MultiArgs | false | 5,471 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
SmallDecoder3_16x | # 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.... | MingSun-Tse/Collaborative-Distillation | SmallDecoder3_16x | false | 14,041 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
SimpleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleModule(torch.nn.Module):
def __init__(self):
super(SimpleModule, self).__init__()
def forward(self, x):
y = x + x
y = y + 2
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleModule | false | 3,341 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
DoubleInputNet | import torch
import torch as t
import torch.nn as nn
class DoubleInputNet(nn.Module):
def __init__(self, firstinsize, secondinsize, outsize, activation=lambda
x: x):
super().__init__()
self.firstinsize = firstinsize
self.secondinsize = secondinsize
self.outsize = outsize
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cbekar/DRL_Project | DoubleInputNet | false | 9,879 | [
"MIT"
] | 0 | 90d197773c7746b253ee7d997d0526e15d05578a | https://github.com/cbekar/DRL_Project/tree/90d197773c7746b253ee7d997d0526e15d05578a |
CPUForgetMult | import torch
from typing import *
class CPUForgetMult(torch.nn.Module):
def __init__(self):
super(CPUForgetMult, self).__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)
prev_h = hidden_init
for i, h in enumerate((f * x).spli... | 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 typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | LaurenSpiegel/fastai_docs | CPUForgetMult | false | 759 | [
"Apache-2.0"
] | 0 | 4fe6b62116d88dea9610548133e6cadb6b260a73 | https://github.com/LaurenSpiegel/fastai_docs/tree/4fe6b62116d88dea9610548133e6cadb6b260a73 |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AdamLohSg/GTA | TokenEmbedding | false | 16,882 | [
"Apache-2.0"
] | 8 | bf6a745a6e28e365466e76360a15ca10ce61e009 | https://github.com/AdamLohSg/GTA/tree/bf6a745a6e28e365466e76360a15ca10ce61e009 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiaxuanYou/graph-pooling | MLP | false | 17,485 | [
"MIT"
] | 5 | e6237f03a72ac55d8a10192ca36fa596973461f5 | https://github.com/JiaxuanYou/graph-pooling/tree/e6237f03a72ac55d8a10192ca36fa596973461f5 |
UpSample | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leak... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | pystokes/depth_estimation | UpSample | false | 4,150 | [
"MIT"
] | 0 | b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 | https://github.com/pystokes/depth_estimation/tree/b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 |
HLCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | anlewy/mt-dnn | HLCriterion | false | 14,864 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
SqueezeAndExcitationModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
return x * x.sigmoid()
class Conv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | debasish-mihup/EfficientConformer | SqueezeAndExcitationModule | false | 10,361 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
MinibatchStdDev | # 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 as th
import torch.nn.parallel
import torch.utils.data
assert_size... | AshwinRJ/Face-Generation-from-Speech | MinibatchStdDev | false | 16,961 | [
"MIT"
] | 4 | 6d8afe8a61185bfe67cd5fd19c7f993630f481b4 | https://github.com/AshwinRJ/Face-Generation-from-Speech/tree/6d8afe8a61185bfe67cd5fd19c7f993630f481b4 |
Depth_Pointwise_Conv1d | import torch
from torch import nn
class Depth_Pointwise_Conv1d(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | rushirajsherlocked/External-Attention-pytorch | Depth_Pointwise_Conv1d | false | 4,214 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
merge | # 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... | Arno3165229/Corner_Traffic_Light | merge | false | 8,873 | [
"BSD-3-Clause"
] | 0 | 91eead49318a3b1e3a9c2295cbe5661cb1074b69 | https://github.com/Arno3165229/Corner_Traffic_Light/tree/91eead49318a3b1e3a9c2295cbe5661cb1074b69 |
Cube | import torch
from torch import nn
class CubeFunctionBackward(torch.autograd.Function):
@staticmethod
def forward(ctx, X, M):
ctx.save_for_backward(X, M)
return M * 3 * X ** 2
@staticmethod
def backward(ctx, V):
X, M = ctx.saved_tensors
return V * 6 * X * M, V * 3 * 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | minhnhat93/didyprog | Cube | false | 16,086 | [
"MIT"
] | 57 | 78886ed939d269b9b2bcb192bf849aa34082880c | https://github.com/minhnhat93/didyprog/tree/78886ed939d269b9b2bcb192bf849aa34082880c |
CNN_decoder_attention | import torch
import torch.nn as nn
import torch.nn.init as init
class CNN_decoder_attention(nn.Module):
def __init__(self, input_size, output_size, stride=2):
super(CNN_decoder_attention, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = nn.R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Qin-Folks/graph-generation | CNN_decoder_attention | false | 2,924 | [
"MIT"
] | 0 | afe1b697272b0e683b4551918de36f57f714e70b | https://github.com/Qin-Folks/graph-generation/tree/afe1b697272b0e683b4551918de36f57f714e70b |
loss_shape_exp | import torch
import torch.nn as nn
class loss_shape_exp(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y, beta=2):
return torch.mean(torch.exp(beta * y) * torch.pow(x - y, 2))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Tsinghua-gongjing/StructureImpute | loss_shape_exp | false | 18,049 | [
"MIT"
] | 9 | 59e33e913998a8841c2cb552828f0f0cc19ebc21 | https://github.com/Tsinghua-gongjing/StructureImpute/tree/59e33e913998a8841c2cb552828f0f0cc19ebc21 |
MiniBatchStd | # 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... | STomoya/animeface | MiniBatchStd | false | 14,366 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
CNN | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class CNN(nn.Module):
""" CNN for heat shock protein classification """
def __init__(self, model_cfg, in_channels, dropout_rate):
super(CNN, self).__init__()
self.embedder = model_cfg.embedder
if self.emb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mswzeus/DeeperHSP | CNN | false | 7,296 | [
"MIT"
] | 1 | 571387f048d3c33fcd78730fdaef57b6c44a27a7 | https://github.com/mswzeus/DeeperHSP/tree/571387f048d3c33fcd78730fdaef57b6c44a27a7 |
SimpleMatmulModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | opti-mix/glow | SimpleMatmulModule | false | 7,405 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
SSE | # 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | IVRL/CCID | SSE | false | 5,316 | [
"MIT"
] | 1 | 0d57c33696da87279d24777a2efd1204f5088bc9 | https://github.com/IVRL/CCID/tree/0d57c33696da87279d24777a2efd1204f5088bc9 |
ClassAttention | import torch
from torch import Tensor
from torch import nn
class ClassAttention(nn.Module):
def __init__(self, dim, num_heads):
super().__init__()
self.num_heads = num_heads
self.scale = (dim // num_heads) ** -0.5
self.q = nn.Linear(dim, dim, bias=False)
self.kv = 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.... | sithu31296/image_classification | ClassAttention | false | 16,462 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision import tran... | justinluyao/phd_thesis | ContrastiveLoss | false | 3,785 | [
"MIT"
] | 0 | 0a61f5deaac86dd34839ce24c2ad89e1411a8540 | https://github.com/justinluyao/phd_thesis/tree/0a61f5deaac86dd34839ce24c2ad89e1411a8540 |
ConditionalBatchNorm2d | # 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 ... | PeterouZh/Omni-GAN-PyTorch | ConditionalBatchNorm2d | false | 14,181 | [
"MIT"
] | 56 | 564a586fed6ce51ef73933d8815d94ce077c4e5c | https://github.com/PeterouZh/Omni-GAN-PyTorch/tree/564a586fed6ce51ef73933d8815d94ce077c4e5c |
EncoderLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hyunseung-Kim/molGCT | EncoderLayer | false | 8,267 | [
"Apache-2.0"
] | 10 | 5a2604337cf0a9d3c725295ccb7c8ea4b0144636 | https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636 |
FastGuidedFilter | import torch
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_x = torch.full((x.data.shape[1], 1, 1, kernel_size)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | HyeongminMoon/copy-paste-aug | FastGuidedFilter | false | 11,492 | [
"MIT"
] | 0 | 38fcd770d70b5d4291de0cbb42073b37d7188537 | https://github.com/HyeongminMoon/copy-paste-aug/tree/38fcd770d70b5d4291de0cbb42073b37d7188537 |
Standardize | # 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.nn import Module
from torch.nn import init
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.... | COMP6248-Reproducability-Challenge/MoveBrick_Reproducibility_DeepSAD | Standardize | false | 11,277 | [
"MIT"
] | 0 | 8985dc9cd8741010362c6ca51e72648b7bd3908f | https://github.com/COMP6248-Reproducability-Challenge/MoveBrick_Reproducibility_DeepSAD/tree/8985dc9cd8741010362c6ca51e72648b7bd3908f |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Lalihoo/yolov5-detect | MetaAconC | false | 9,620 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
NCutLossOptimized | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Christer-L/wnet_pytorch | NCutLossOptimized | false | 279 | [
"MIT"
] | 0 | c7a7d3db0c07d5e2d83fe152ce5fdae31472748b | https://github.com/Christer-L/wnet_pytorch/tree/c7a7d3db0c07d5e2d83fe152ce5fdae31472748b |
AttendNodeModule | # 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.... | qiuyue1993/XNM-Net | AttendNodeModule | false | 16,297 | [
"MIT"
] | 95 | 1c4a16fd745d9e90e0d7a08b21e7efca4d2c6195 | https://github.com/qiuyue1993/XNM-Net/tree/1c4a16fd745d9e90e0d7a08b21e7efca4d2c6195 |
ScoreCap | import torch
from torch import nn
import torch.nn
import torch.optim
class ScoreCap(nn.Module):
def __init__(self, cap: 'float'):
super().__init__()
self.cap = cap
def forward(self, input):
return torch.clip(input, max=self.cap)
def get_inputs():
return [torch.rand([4, 4, 4, 4]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dy... | BerenLuthien/ReAgent | ScoreCap | false | 13,382 | [
"BSD-3-Clause"
] | 1,156 | 52f666670a7fa03206812ef48949f6b934d400f7 | https://github.com/BerenLuthien/ReAgent/tree/52f666670a7fa03206812ef48949f6b934d400f7 |
eSEModule | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Tanveer81/BoxVOS | eSEModule | false | 17,983 | [
"BSD-2-Clause"
] | 4 | c30aa319f18f3fbee2a25e0ed25cb006a4598300 | https://github.com/Tanveer81/BoxVOS/tree/c30aa319f18f3fbee2a25e0ed25cb006a4598300 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | fadamsyah/pytorch-brain-mri-segmentation | DiceLoss | false | 6,674 | [
"MIT"
] | 1 | bdb310ecacbddfce2cef20d50cf0b638dd1bc7b1 | https://github.com/fadamsyah/pytorch-brain-mri-segmentation/tree/bdb310ecacbddfce2cef20d50cf0b638dd1bc7b1 |
FusedDownsample | # 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 math import sqrt
assert_size_stride = torch._C._dynam... | KwonGihyun/DiagonalGAN | FusedDownsample | false | 8,443 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
DPSLTMAdapter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | adriansarstedt/opacus | DPSLTMAdapter | false | 12,097 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 |
LuongAttention | import torch
import torch.nn.functional as F
from torch import nn
class LuongAttention(nn.Module):
"""
Luong Attention from Effective Approaches to Attention-based Neural Machine Translation
https://arxiv.org/pdf/1508.04025.pdf
"""
def __init__(self, attention_dim):
super(LuongAttention, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aditya140/ques_gen | LuongAttention | false | 18,212 | [
"MIT"
] | 3 | 57be43de682a384ee4114adb3fbc75a527f2aaff | https://github.com/aditya140/ques_gen/tree/57be43de682a384ee4114adb3fbc75a527f2aaff |
UNETWithoutPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | quenting44/semantic_segmentation | UNETWithoutPooling | false | 10,824 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
BinarizeActivations | # 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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.da... | adityakusupati/LLC-2.0 | BinarizeActivations | false | 18,219 | [
"MIT"
] | 10 | 38608bbaa425b15dcf5c971000b7a1b08120fb5c | https://github.com/adityakusupati/LLC-2.0/tree/38608bbaa425b15dcf5c971000b7a1b08120fb5c |
InstanceNormLayer | import torch
import torch.nn as nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if x.ndim != 4:
raise ValueError(
f'The... | 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_... | Hsintien-Ng/idinvert_pytorch-reproduced | InstanceNormLayer | false | 8,253 | [
"MIT"
] | 20 | cf3302510573138cf16202add06feae7c93624ea | https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea |
Attention | import math
import torch
import torch.nn as nn
import torch as t
class Linear(nn.Module):
"""
Linear Module
"""
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
"""
:param in_dim: dimension of input
:param out_dim: dimension of output
:param bias: boole... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kongziyue1234/mooc | Attention | false | 3,855 | [
"MIT"
] | 0 | 3b0c822dd55c1066cbc829137e6c424dcda5067e | https://github.com/kongziyue1234/mooc/tree/3b0c822dd55c1066cbc829137e6c424dcda5067e |
FIN2dCyclic | # 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.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | cv-rits/CoMoGAN | FIN2dCyclic | false | 15,091 | [
"Apache-2.0"
] | 141 | 09f2f0f694421e289fcad467ca0b23f52e4da7a4 | https://github.com/cv-rits/CoMoGAN/tree/09f2f0f694421e289fcad467ca0b23f52e4da7a4 |
Actor | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Actor(nn.Module):
def __init__(self, state_size, action_size, args, log_std_min=-20,
log_std_max=2):
super(Actor, self).__init__()
self.log_std_min = log_std_min
self.log_std_max = log_std_max
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dongminlee94/Samsung-DRL-Code | Actor | false | 15,198 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 |
Word2Vec | import torch
import torch.nn as nn
import torch.nn.functional as F
class Word2Vec(nn.Module):
def __init__(self, vocabulary_size, embedding_size):
super(Word2Vec, self).__init__()
self.w1 = nn.Parameter(torch.randn(vocabulary_size, embedding_size,
requires_grad=True))
self.w2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abhinavbh08/NNTI-WS2021-NLP-Project | Word2Vec | false | 9,658 | [
"MIT"
] | 0 | 946cfdcb0e0e64969d12423fa1b26dad3cb2d417 | https://github.com/abhinavbh08/NNTI-WS2021-NLP-Project/tree/946cfdcb0e0e64969d12423fa1b26dad3cb2d417 |
CausalAttentionSortNet | import torch
from torch.nn import functional as F
from torch import nn
def bucket(buckets, t, dim=1):
shape = list(t.shape)
shape[dim:dim + 1] = [buckets, -1]
return t.reshape(*shape)
def differentiable_topk(x, k, temperature=1.0):
*_, n, dim = x.shape
topk_tensors = []
for i in range(k):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tatp22/sinkhorn-transformer | CausalAttentionSortNet | false | 13,040 | [
"MIT"
] | 0 | 3eaa76e99efeee75cf8298defaaef51621c55ff4 | https://github.com/tatp22/sinkhorn-transformer/tree/3eaa76e99efeee75cf8298defaaef51621c55ff4 |
VirtualBatchNormNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils
import torch.utils.data
from tor... | Silent-Zebra/JEM | VirtualBatchNormNN | false | 17,962 | [
"Apache-2.0"
] | 6 | 33440aff8429d9a24a8ba858d0209f4b48be8e05 | https://github.com/Silent-Zebra/JEM/tree/33440aff8429d9a24a8ba858d0209f4b48be8e05 |
EqualizedLinear | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Parameter... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
impo... | Aarsh2001/annotated_deep_learning_paper_implementations | EqualizedLinear | false | 4,762 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
Conv2dDynamicSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Conv2dDynamicSamePadding(nn.Conv2d):
""" 2D Convolutions like TensorFlow, for a dynamic image size """
def __init__(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.parallel
import torch.optim
import torch.u... | BradleyBrown19/CustomObjectDetector | Conv2dDynamicSamePadding | false | 2,092 | [
"Apache-2.0"
] | 0 | 11c14ec6127c553ac365703c768b75dde33d9a4d | https://github.com/BradleyBrown19/CustomObjectDetector/tree/11c14ec6127c553ac365703c768b75dde33d9a4d |
TemperatureHolder | import torch
from torch import nn
class TemperatureHolder(nn.Module):
"""Module that holds a temperature as a learnable value.
Args:
initial_log_temperature (float): Initial value of log(temperature).
"""
def __init__(self, initial_log_temperature=0):
super().__init__()
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | lin826/pfrl | TemperatureHolder | false | 12,708 | [
"MIT"
] | 0 | 62d7f13b854f1879211a386fd870a7db982cc8ec | https://github.com/lin826/pfrl/tree/62d7f13b854f1879211a386fd870a7db982cc8ec |
patch_extractor | import torch
from torch import nn
class patch_extractor(nn.Module):
"""
Module for creating custom patch extractor
"""
def __init__(self, patch_size, pad=False, center=False, dim=2):
super(patch_extractor, self).__init__()
self.dim = dim
self.im2pat = nn.Unfold(kernel_size=pat... | 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... | johertrich/Wasserstein_Patch_Prior | patch_extractor | false | 6,968 | [
"MIT"
] | 1 | 70877a6f1031e51b7868984b97027951d1d190d3 | https://github.com/johertrich/Wasserstein_Patch_Prior/tree/70877a6f1031e51b7868984b97027951d1d190d3 |
SAB | # 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.... | ClintvanHoesel/MXMNet_adapted | SAB | false | 344 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
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.... | AppleHolic/pytorch_sound | MultiHeadAttention | false | 13,317 | [
"BSD-2-Clause"
] | 86 | 2320516d21d70c406d1dee74927e238972c96106 | https://github.com/AppleHolic/pytorch_sound/tree/2320516d21d70c406d1dee74927e238972c96106 |
PreprocessAtari | import torch
from torch import nn
class PreprocessAtari(nn.Module):
def forward(self, x):
x = x.permute(0, 3, 1, 2).contiguous()
return x / 255.0
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | SemyonSemenov/mipt-rl-hw-2022 | PreprocessAtari | false | 9,421 | [
"MIT"
] | 0 | 923fd0b7e3f900c1a91ddf256c9b6f53a62d1653 | https://github.com/SemyonSemenov/mipt-rl-hw-2022/tree/923fd0b7e3f900c1a91ddf256c9b6f53a62d1653 |
InvConvNear | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = 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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | InvConvNear | false | 11,632 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling)... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CarnoZhao/mmsegmentation | InputInjection | false | 7,845 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
EncoderImagePrecomp | # 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 numpy as np
... | Shiyang-Yan/Discrete-continous-PG-for-Retrieval | EncoderImagePrecomp | false | 17,924 | [
"Apache-2.0"
] | 8 | 39fd7a81f732ae043c2ea20352a0c55b72834639 | https://github.com/Shiyang-Yan/Discrete-continous-PG-for-Retrieval/tree/39fd7a81f732ae043c2ea20352a0c55b72834639 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, d_head):
super(ScaledDotProductAttention, self).__init__()
self.d_head = d_head
self.attention_dropout = nn.Dropout(p=0.1)
def forward(self, q, k, v, mask=None):
atte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | francismontalbo/attention-is-all-you-need-paper | MultiHeadAttention | false | 15,377 | [
"MIT"
] | 167 | 21ba3e48917da0c6808126d183bece6a9969cfd2 | https://github.com/francismontalbo/attention-is-all-you-need-paper/tree/21ba3e48917da0c6808126d183bece6a9969cfd2 |
PrototypicalNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.parallel
def L2SquareDist(A, B, average=True):
assert A.dim() == 3
assert B.dim() == 3
assert A.size(0) == B.size(0) and A.size(2) == B.size(2)
nB = A.size(0)
Na = A.size(1)
Nb =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.optim
import torch.nn.parallel
assert_size_st... | Basasuya/FewShotWithoutForgetting | PrototypicalNetwork | false | 3,469 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
PDF | import math
import torch
import torch.nn as nn
def compute_negative_ln_prob(Y, mu, ln_var, pdf):
var = ln_var.exp()
if pdf == 'gauss':
negative_ln_prob = 0.5 * ((Y - mu) ** 2 / var).sum(1).mean(
) + 0.5 * Y.size(1) * math.log(2 * math.pi) + 0.5 * ln_var.sum(1
).mean()
elif ... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | wangxuuu/Demo | PDF | false | 13,096 | [
"MIT"
] | 0 | f1d85a55525a4199d63ee7dfe0ae2f21d3066c7c | https://github.com/wangxuuu/Demo/tree/f1d85a55525a4199d63ee7dfe0ae2f21d3066c7c |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | MarouaJaoua/cells-nuclei-segmentation | DiceLoss | false | 9,349 | [
"MIT"
] | 0 | 09d65db104a7297ec6f4c975b668bb7ca93c7372 | https://github.com/MarouaJaoua/cells-nuclei-segmentation/tree/09d65db104a7297ec6f4c975b668bb7ca93c7372 |
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.... | THinnerichs/pytorch_geometric | Attention | false | 11,915 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
LinearModel | # 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... | VVKot/mlinseconds-find-me | LinearModel | false | 11,951 | [
"MIT"
] | 0 | f50ec09ef5cef23b694970a9a975f7a0f8c59b76 | https://github.com/VVKot/mlinseconds-find-me/tree/f50ec09ef5cef23b694970a9a975f7a0f8c59b76 |
GradientLoss | # 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
... | arpan-dhatt/oidn | GradientLoss | false | 14,896 | [
"Apache-2.0"
] | 1,206 | 9419411ba4b343b475b53587cadd44c83d68dc2a | https://github.com/arpan-dhatt/oidn/tree/9419411ba4b343b475b53587cadd44c83d68dc2a |
ReLUDropout | # 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.utils.data
import torch.cuda
import torch.utils.checkpoint
assert_size_strid... | mullovc/NMTGMinor | ReLUDropout | false | 4,038 | [
"MIT"
] | 0 | b1b7b1e018eaa0d99a43449655937cc050a29987 | https://github.com/mullovc/NMTGMinor/tree/b1b7b1e018eaa0d99a43449655937cc050a29987 |
BarlowTwinsLoss | import torch
from torch import nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class BarlowTwinsLoss(nn.Module):
"""The Contrastive embedding loss.
It has been proposed in `Barlow Twins:
Self-Supe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | gavrin-s/catalyst | BarlowTwinsLoss | false | 12,414 | [
"Apache-2.0"
] | 0 | 81087d8348b359e501d899f7a8350e0bedfc2b7d | https://github.com/gavrin-s/catalyst/tree/81087d8348b359e501d899f7a8350e0bedfc2b7d |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | MCplayerFromPRC/CodeT5 | RobertaClassificationHead | false | 1,423 | [
"BSD-3-Clause"
] | 0 | 726ed4a4221be125b6083439667573ef5fef6984 | https://github.com/MCplayerFromPRC/CodeT5/tree/726ed4a4221be125b6083439667573ef5fef6984 |
EntmaxBisect | from torch.autograd import Function
import torch
import torch.nn as nn
def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True):
"""alpha-entmax: normalizing sparse transform (a la softmax).
Solves the optimization problem:
max_p <x, p> - H_a(p) s.t. p >= 0, sum(p) == 1.
wh... | 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.autograd import F... | cifkao/entmax | EntmaxBisect | false | 15,222 | [
"MIT"
] | 298 | f18bab9318f9d2471a36545ee0b4c97be6d48a87 | https://github.com/cifkao/entmax/tree/f18bab9318f9d2471a36545ee0b4c97be6d48a87 |
DiaynDiscrimNet | # 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... | AswinRetnakumar/Machina | DiaynDiscrimNet | false | 13,316 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
Net16 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net16(nn.Module):
def __init__(self, input_dim, output_dim):
super(Net16, self).__init__()
self.linear1 = nn.Linear(input_dim, 16)
self.linear2 = nn.Linear(16, output_dim)
def forward(self, x):
x = F.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gautam-sharma1/Imitation-Learning | Net16 | false | 3,522 | [
"MIT"
] | 0 | 20b6fcd2a8d6de8eb95e6831f5b379a083306361 | https://github.com/gautam-sharma1/Imitation-Learning/tree/20b6fcd2a8d6de8eb95e6831f5b379a083306361 |
LayerNorm | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, d):
super().__init__()
self.a = nn.Parameter(torch.ones(d).unsqueeze(0).unsqueeze(0))
self.b = nn.Parameter(torch.zeros(d).unsqueeze(0).unsqueeze(0))
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.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | xyc1207/benchmarking-gnns | LayerNorm | false | 16,755 | [
"MIT"
] | 1,809 | 9ba25a2825e8c155a93730d6e8f8752090292942 | https://github.com/xyc1207/benchmarking-gnns/tree/9ba25a2825e8c155a93730d6e8f8752090292942 |
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