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 |
|---|---|---|---|---|---|---|---|---|---|---|
ConvEncoder3D | import torch
from torch import nn
class ConvEncoder3D(nn.Module):
""" Simple convolutional conditioning network.
It consists of 6 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
"""
def __init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | BoyanJIANG/4D-Compositional-Representation | ConvEncoder3D | false | 7,864 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | RepeatModule | false | 12,555 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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 |
DepthLogLoss | import torch
import torch.nn as nn
class DepthLogLoss(nn.Module):
def __init__(self, balance_factor):
super(DepthLogLoss, self).__init__()
self.balance_factor = balance_factor
def forward(self, inputs, targets):
n, _, h, w = inputs.shape
n_pixel = n * h * w
inputs = t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | pystokes/depth_estimation | DepthLogLoss | false | 4,140 | [
"MIT"
] | 0 | b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 | https://github.com/pystokes/depth_estimation/tree/b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 |
SimpleMatmulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMatmulModule(torch.nn.Module):
def __init__(self):
super(SimpleMatmulModule, self).__init__()
def forward(self, a, b):
return a.matmul(b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 |
BertSelfAttention | # 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.... | Ago3/VLP | BertSelfAttention | false | 9,980 | [
"Apache-2.0"
] | 0 | 4dec0e04b8592f4a74fe66c253dbb92574e7e2ba | https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba |
RobertaAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from typing import Tuple
from typing import List
from typing import Set
def find_pruneable_heads_and_indices(heads: 'List[int]', n_heads: 'int',
head_size: 'int', already_pruned_heads: 'Set[int]') ->Tuple[Set[int],
to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BlackNoodle/TUCORE-GCN | RobertaAttention | false | 8,808 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
AdaptiveMaxPool2d | import torch
import torch.nn as nn
class AdaptiveMaxPool2d(nn.Module):
def __init__(self):
super(AdaptiveMaxPool2d, self).__init__()
self.layer = nn.AdaptiveMaxPool2d((5, 7))
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | yifanpu001/PytorchToCaffe | AdaptiveMaxPool2d | false | 4,711 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
MonomialNN | # 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
from warnings import warn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._... | Tiamat-Tech/neurodiffeq | MonomialNN | false | 14,495 | [
"MIT"
] | 202 | 622827e5b9b65d285ebe36614fbdae68ba07f4dc | https://github.com/Tiamat-Tech/neurodiffeq/tree/622827e5b9b65d285ebe36614fbdae68ba07f4dc |
My_SmoothL1Loss | # 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
assert_size_stride = t... | theleokul/AWR-Adaptive-Weighting-Regression | My_SmoothL1Loss | false | 4,415 | [
"MIT"
] | 0 | a6c224302bab474db8b774a2d009c9497e32f6bd | https://github.com/theleokul/AWR-Adaptive-Weighting-Regression/tree/a6c224302bab474db8b774a2d009c9497e32f6bd |
cPReLU | import torch
import torch.nn as nn
class cPReLU(nn.Module):
def __init__(self, complex_axis=1):
super(cPReLU, self).__init__()
self.r_prelu = nn.PReLU()
self.i_prelu = nn.PReLU()
self.complex_axis = complex_axis
def forward(self, inputs):
real, imag = torch.chunk(inpu... | 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... | JamesLiao714/FullSubNet | cPReLU | false | 605 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
SoftmaxAttention | import torch
import torch.nn as nn
def masked_softmax(tensor, mask):
"""
Apply a masked softmax on the last dimension of a tensor.
The input tensor and mask should be of size (batch, *, sequence_length).
Args:
tensor: The tensor on which the softmax function must be applied along
t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YJiangcm/Chinese-sentence-pair-modeling | SoftmaxAttention | false | 14,626 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
Classify | import torch
import torch.nn as nn
import torch.utils.data
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Arui66/FPSAutomaticAiming | Classify | false | 13,304 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
MFBFusion | from _paritybench_helpers import _mock_config
import time
import torch
from torch import nn
class BaseModel(nn.Module):
def __init__(self):
super(BaseModel, self).__init__()
self.model_name = str(type(self))
def load(self, path):
self.load_state_dict(torch.load(path))
def save(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
import time
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | chorseng/UMD | MFBFusion | false | 15,039 | [
"MIT"
] | 48 | 680681fea76abcea02ff5f351727bcbb468c372a | https://github.com/chorseng/UMD/tree/680681fea76abcea02ff5f351727bcbb468c372a |
upsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import 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 | upsample | false | 8,882 | [
"BSD-3-Clause"
] | 0 | 91eead49318a3b1e3a9c2295cbe5661cb1074b69 | https://github.com/Arno3165229/Corner_Traffic_Light/tree/91eead49318a3b1e3a9c2295cbe5661cb1074b69 |
Color_MNIST_CNN | # 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.... | VinAIResearch/mDSDI | Color_MNIST_CNN | false | 18,081 | [
"Apache-2.0"
] | 9 | 8ec49085d8389ab490ec633c3ae4bf66be085366 | https://github.com/VinAIResearch/mDSDI/tree/8ec49085d8389ab490ec633c3ae4bf66be085366 |
SimpleMinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMinModule(torch.nn.Module):
def __init__(self):
super(SimpleMinModule, self).__init__()
def forward(self, a, b):
return torch.min(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([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
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleMinModule | false | 12,582 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
PatchEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | sithu31296/image_classification | PatchEmbedding | false | 16,464 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
BiDAFAttention | # 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.... | HakobJak/ml-mipt | BiDAFAttention | false | 13,746 | [
"MIT"
] | 440 | ab0cbd5d553e9da309bda54d35b4e93a8eb99696 | https://github.com/HakobJak/ml-mipt/tree/ab0cbd5d553e9da309bda54d35b4e93a8eb99696 |
RelPositionMultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | WenjingXia/wenet | RelPositionMultiHeadedAttention | false | 1,248 | [
"Apache-2.0"
] | 0 | 9a1fd005cd06be16518a5476076b2ae6af2ec41a | https://github.com/WenjingXia/wenet/tree/9a1fd005cd06be16518a5476076b2ae6af2ec41a |
OELoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class OELoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits):
return -torch.log_softmax(logits, 1).mean(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AlexMeinke/Provable-OOD-Detection | OELoss | false | 7,705 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EVASHINJI/Seq2Seq-PyTorch | Attention | false | 17,233 | [
"Apache-2.0"
] | 4 | d53f8d7c240bd7c16ebd1475384774bd064b4b03 | https://github.com/EVASHINJI/Seq2Seq-PyTorch/tree/d53f8d7c240bd7c16ebd1475384774bd064b4b03 |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TabbenBenchmark/tabben | GEGLU | false | 17,963 | [
"MIT"
] | 5 | d74114afc4b6f67be488ab6bf8ad6fd316fdb888 | https://github.com/TabbenBenchmark/tabben/tree/d74114afc4b6f67be488ab6bf8ad6fd316fdb888 |
ChannelMixer | # 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... | Cardroid/Muskits | ChannelMixer | false | 8,981 | [
"Apache-2.0"
] | 0 | 91708bb243bc671e48893a734aee710c356e4bd8 | https://github.com/Cardroid/Muskits/tree/91708bb243bc671e48893a734aee710c356e4bd8 |
Attention_layer | import torch
from torch import nn
class Attention_layer(nn.Module):
def __init__(self, sequence_length):
super(Attention_layer, self).__init__()
self.input_size = sequence_length
self.output_size = sequence_length
self.dense = nn.Linear(sequence_length, sequence_length)
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.... | w6688j/ChatBot-PyTorch | Attention_layer | false | 13,080 | [
"Apache-2.0"
] | 0 | 84f5a3267d16c650b90727ce80e4952901faa902 | https://github.com/w6688j/ChatBot-PyTorch/tree/84f5a3267d16c650b90727ce80e4952901faa902 |
ResidualBlock | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | GEN418/EventGAN | ResidualBlock | false | 484 | [
"MIT"
] | 0 | 372318bc8f285f513db4babf7786b5c04e97c86d | https://github.com/GEN418/EventGAN/tree/372318bc8f285f513db4babf7786b5c04e97c86d |
inplace_softmax | import torch
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class inplace_softmax(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x1 = x + 1
x2 = nn.Softmax(dim=-1)(x1)
return x2
def get_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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.cuda
impo... | JudeDavis1/intel-extension-for-pytorch | inplace_softmax | false | 2,575 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
SpatialSoftmax | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ozcell/ENet-SAD_Pytorch | SpatialSoftmax | false | 16,209 | [
"MIT"
] | 53 | aaa79b5e96316e1bf24d3c2147ee622d4f17bc24 | https://github.com/ozcell/ENet-SAD_Pytorch/tree/aaa79b5e96316e1bf24d3c2147ee622d4f17bc24 |
SelectiveMarginLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class SelectiveMarginLoss(nn.Module):
def __init__(self, loss_weight=5e-05, margin=0.2):
super(SelectiveMarginLoss, self).__init__()
self.margin = margin
self.loss_weight = loss_weight
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Dogacel/mmfashion | SelectiveMarginLoss | false | 11,411 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
CrossUnit | # 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... | LSTM-Kirigaya/NUAA-guandan | CrossUnit | false | 9,216 | [
"MIT"
] | 0 | f6920868649c26536b3dc3fce8ecd1d4f7c755fa | https://github.com/LSTM-Kirigaya/NUAA-guandan/tree/f6920868649c26536b3dc3fce8ecd1d4f7c755fa |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | JunKong5/WestBERT | SelfAttention | false | 9,168 | [
"MIT"
] | 0 | 8e0fc9aca290103698cd08239710193c36b06eff | https://github.com/JunKong5/WestBERT/tree/8e0fc9aca290103698cd08239710193c36b06eff |
GeneralizedMeanPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | gmt710/fast-reid | GeneralizedMeanPooling | false | 12,441 | [
"Apache-2.0"
] | 0 | 44a609280013eb6928f67c418c7212d67e40fb5d | https://github.com/gmt710/fast-reid/tree/44a609280013eb6928f67c418c7212d67e40fb5d |
TemperatureTanh | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Mingxiao-Li/vln-ce-eval | TemperatureTanh | false | 2,652 | [
"MIT"
] | 0 | 2217513e9d9b6352bf0939d3b76a359c64e89dda | https://github.com/Mingxiao-Li/vln-ce-eval/tree/2217513e9d9b6352bf0939d3b76a359c64e89dda |
Arborist | import torch
import torch.nn as nn
class Arborist(nn.Module):
def __init__(self, l_dim, r_dim, k=5):
super(Arborist, self).__init__()
self.u = nn.Linear(l_dim * 2, k, bias=False)
self.W = nn.Bilinear(l_dim * 2, r_dim, k, bias=False)
def forward(self, e1, e2, q):
"""
e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | QingkaiZeng/GenTaxo | Arborist | false | 8,722 | [
"MIT"
] | 28 | 10257a1714d14c6a4c49cbfa0b507408f718cdf0 | https://github.com/QingkaiZeng/GenTaxo/tree/10257a1714d14c6a4c49cbfa0b507408f718cdf0 |
BackboneModel1 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class BackboneModel1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_inputs():
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | savan77/nni | BackboneModel1 | false | 4,272 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
def forward(self, x):
x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | YaronBenAtar/glow | Foo | false | 14,673 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
PKT | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | kctsiolis/RepDistiller | PKT | false | 3,932 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
WeldonPooling | # 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... | maxgreat/dsve-loc | WeldonPooling | false | 16,024 | [
"BSD-3-Clause-Clear"
] | 56 | dd6807d02c0d5fd3e215be8e5c7a88e73102e561 | https://github.com/maxgreat/dsve-loc/tree/dd6807d02c0d5fd3e215be8e5c7a88e73102e561 |
L2Norm | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | Feywell/association_lstm_implement | L2Norm | false | 5,161 | [
"MIT"
] | 1 | 4e439bd934dc865aad0015a897980a8f124602af | https://github.com/Feywell/association_lstm_implement/tree/4e439bd934dc865aad0015a897980a8f124602af |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Critic, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_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.... | gntoni/pytorch-ddpg-naf | Critic | false | 12,466 | [
"MIT"
] | 0 | d208d0c0c38a9d2d2041f1e7e95695359eba430e | https://github.com/gntoni/pytorch-ddpg-naf/tree/d208d0c0c38a9d2d2041f1e7e95695359eba430e |
SparseConv2d | # 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.... | Sense-GVT/BigPretrain | SparseConv2d | false | 17,920 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
CosLoss | # 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
assert... | lonePatient/TorchBlocks | CosLoss | false | 15,936 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
IDiv | # 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
@triton.jit
def triton_poi_fused_div_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | PogChamper/torch2trt | IDiv | false | 14,196 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
ResizeConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | sanghiad/vae_vampprior | ResizeConv2d | false | 16,372 | [
"MIT"
] | 218 | d24bc0c8781b7ee7b9570c2d560e43bceff50da4 | https://github.com/sanghiad/vae_vampprior/tree/d24bc0c8781b7ee7b9570c2d560e43bceff50da4 |
SegmentationLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LovaszHingeLoss(nn.Module):
"""
This class implements the lovasz hinge loss which is the continuous of the IoU for binary segmentation.
Source: https://github.com/bermanmaxim/LovaszSoftmax
"""
def __init__(self) ->None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChristophReich1996/Cell-DETR | SegmentationLoss | false | 13,668 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | JamesWang007/Dive-into-DL-PyTorch | GlobalAvgPool2d | false | 5,372 | [
"Apache-2.0"
] | 1 | 267b54168322ab37da44e83008fba4f24b70fa9f | https://github.com/JamesWang007/Dive-into-DL-PyTorch/tree/267b54168322ab37da44e83008fba4f24b70fa9f |
CPULayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Smerity/pytorch-qrnn | CPULayerNorm | false | 17,928 | [
"BSD-3-Clause"
] | 4 | 907c8ea53f689136fcc50996b6474de967745202 | https://github.com/Smerity/pytorch-qrnn/tree/907c8ea53f689136fcc50996b6474de967745202 |
ShiftBias | import torch
import torch.nn as nn
class ShiftBias(nn.Module):
def __init__(self, bias):
super(ShiftBias, self).__init__()
self.bias = bias
def forward(self, x):
return x + self.bias
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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | yuangan/A2L | ShiftBias | false | 4,637 | [
"MIT"
] | 0 | 8cbc9b5f368924c8c75cbab53e9bb10dcf265c7e | https://github.com/yuangan/A2L/tree/8cbc9b5f368924c8c75cbab53e9bb10dcf265c7e |
wide_basic | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | FloralZhao/unlimited_labeled_data_project_strong_augmen | wide_basic | false | 509 | [
"MIT"
] | 0 | caf90d70145e5d841a38b2e2f18a710703264a28 | https://github.com/FloralZhao/unlimited_labeled_data_project_strong_augmen/tree/caf90d70145e5d841a38b2e2f18a710703264a28 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
def focal_loss(input_values, gamma):
"""Computes the focal loss"""
p = torch.exp(-input_values)
loss = (1 - p) ** gamma * input_values
return loss.mean()
class Focal... | 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
... | raman32/LDAM-DRW | FocalLoss | false | 7,530 | [
"MIT"
] | 1 | 7ce2251c01b94c7259108a1e188457f0b720651d | https://github.com/raman32/LDAM-DRW/tree/7ce2251c01b94c7259108a1e188457f0b720651d |
FCNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Johnsonms/NNI_master | FCNet | false | 11,609 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
CoPredictor | import torch
import torch.autograd
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.autogr... | dumpmemory/W2NER | CoPredictor | false | 15,291 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda |
PyTorchMLP | import torch
import torch.nn as nn
class PyTorchMLP(nn.Module):
"""
A feed forward network to make single step predictions on 1D time series data.
"""
def __init__(self, inputsize, prefix):
super(PyTorchMLP, self).__init__()
self.fc1 = nn.Linear(in_features=inputsize, out_features=rou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | danvran/ASM | PyTorchMLP | false | 9,955 | [
"MIT"
] | 0 | e678fa507f847ec2ff947ec4ca123858ffe46d4d | https://github.com/danvran/ASM/tree/e678fa507f847ec2ff947ec4ca123858ffe46d4d |
encoderVH | import torch
import torch.nn as nn
import torch.nn.functional as F
class encoderVH(nn.Module):
def __init__(self):
super(encoderVH, self).__init__()
self.conv1 = nn.Conv3d(in_channels=1, out_channels=64, kernel_size=
4, stride=2, padding=1, bias=True)
self.gn1 = nn.GroupNorm(4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Miles629/TransparentShapeRealData | encoderVH | false | 14,051 | [
"MIT"
] | 91 | b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | https://github.com/Miles629/TransparentShapeRealData/tree/b81098a2d1882f5fd33fba6167d7258dbe02d6d2 |
NegativeLearningLoss | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils
import torch.distributed
class NegativeLearningLoss(nn.Module):
def __init__(self, threshold=0.05):
super(NegativeLearningLoss, self).__init__()
self.threshold = threshold
def forward(self, predict):
ma... | 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
... | BIT-DA/RIPU | NegativeLearningLoss | false | 16,953 | [
"MIT"
] | 9 | 125edf112c9ded1e7497aedb2a092331824df100 | https://github.com/BIT-DA/RIPU/tree/125edf112c9ded1e7497aedb2a092331824df100 |
ReconstructionLoss | # 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 functools import reduce
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019 | ReconstructionLoss | false | 8,607 | [
"MIT"
] | 12 | b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 | https://github.com/NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019/tree/b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 |
L2Norm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | Steffen-Wolf/vit-pytorch | L2Norm | false | 9,609 | [
"MIT"
] | 0 | 4f590b9bd570091d9070a039ad33301516caa341 | https://github.com/Steffen-Wolf/vit-pytorch/tree/4f590b9bd570091d9070a039ad33301516caa341 |
BoundCos | # 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 math
import numpy as np
import torch.nn as nn
import torch.nn.func... | Mahoumaru/auto_LiRPA | BoundCos | false | 13,228 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
CatImgs | import torch
from torch import nn
class CatImgs(nn.Module):
def forward(self, img1, img2, img3):
return torch.cat((img1, img2, img3), 3)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | crisdeodates/AI-depthai-experiments | CatImgs | false | 6,484 | [
"MIT"
] | 1 | 74b8b84a03cb637d20a7fcd091cce11add78bd2c | https://github.com/crisdeodates/AI-depthai-experiments/tree/74b8b84a03cb637d20a7fcd091cce11add78bd2c |
Weighed_Bce_Loss | import torch
import torch.nn.functional as F
from torch import nn
class Weighed_Bce_Loss(nn.Module):
def __init__(self):
super(Weighed_Bce_Loss, self).__init__()
def forward(self, x, label):
x = x.view(-1, 1, x.shape[1], x.shape[2])
label = label.view(-1, 1, label.shape[1], label.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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | suyukun666/UFO | Weighed_Bce_Loss | false | 16,513 | [
"MIT"
] | 122 | e57016948b03cd2f75155d2958cea69b6e4b56f8 | https://github.com/suyukun666/UFO/tree/e57016948b03cd2f75155d2958cea69b6e4b56f8 |
resblock | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | BradyFU/DVG | resblock | false | 13,416 | [
"MIT"
] | 102 | 53fd50cdc51d783b33394726b8f8a2b2216f157b | https://github.com/BradyFU/DVG/tree/53fd50cdc51d783b33394726b8f8a2b2216f157b |
DecoderLayer | # 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.... | davide-belli/generative-graph-transformer | DecoderLayer | false | 15,166 | [
"MIT"
] | 51 | 949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 | https://github.com/davide-belli/generative-graph-transformer/tree/949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 |
Linear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | collector-m/LiDAR-MOS | Linear | false | 15,068 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
SelfAttn | # 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.... | TuBui/deep_image_comparator | SelfAttn | false | 1,169 | [
"MIT"
] | 0 | 2dea7738d794b91a960ee9f41461a4e3ffcd5e44 | https://github.com/TuBui/deep_image_comparator/tree/2dea7738d794b91a960ee9f41461a4e3ffcd5e44 |
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.... | congson1293/Transformer | EncoderLayer | false | 1,762 | [
"Apache-2.0"
] | 0 | 249638f3287e0ed11c71496178fe2ceac2d758df | https://github.com/congson1293/Transformer/tree/249638f3287e0ed11c71496178fe2ceac2d758df |
AlterCoAttn | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.autograd
import torch.nn
class Attn(nn.Module):
"""
Unit attention operation for alternating co-attention.
``https://arxiv.org/pdf/1606.00061.pdf``
.. math::
\\begin{array}{ll}
H = \\tanh(W_x * X + (W_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HCY123902/visdial-gnn | AlterCoAttn | false | 8,229 | [
"MIT"
] | 44 | c38090c672cdf04a4fabe139f96d944fd82cb123 | https://github.com/HCY123902/visdial-gnn/tree/c38090c672cdf04a4fabe139f96d944fd82cb123 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self, gamma=2, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = nn.CrossEntropyLoss(reduction='none')
def forward(self, input, target):
logp = self.ce(... | 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
... | delldu/EQFace | FocalLoss | false | 6,543 | [
"MIT"
] | 1 | a088e80709c1e31a57e302cabfa85ab96f2c0aa5 | https://github.com/delldu/EQFace/tree/a088e80709c1e31a57e302cabfa85ab96f2c0aa5 |
BertIntermediate | # 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
import ... | VinodS7/sota-music-tagging-models | BertIntermediate | false | 14,584 | [
"MIT"
] | 199 | 6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 | https://github.com/VinodS7/sota-music-tagging-models/tree/6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 |
Sine | import torch
import torch.utils.data
import torch.nn as nn
class Sine(nn.Module):
def __init(self):
super().__init__()
def forward(self, input):
return torch.sin(30 * input)
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.utils.data
import torch.nn as nn
assert_size_stride = torch.... | RisingStockPrices/sirenized-deepsdf | Sine | false | 11,808 | [
"MIT"
] | 0 | c0fb33e26b6bf0753c02adc5186af344e40a6d04 | https://github.com/RisingStockPrices/sirenized-deepsdf/tree/c0fb33e26b6bf0753c02adc5186af344e40a6d04 |
GenNoise | import torch
import torch.nn as nn
import torch.nn.init
class GenNoise(nn.Module):
def __init__(self, dim2):
super(GenNoise, self).__init__()
self.dim2 = dim2
def forward(self, input):
a = list(input.size())
a[1] = self.dim2
b = torch.zeros(a).type_as(input.data)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DDQXZcp/FYP_ProjectFile_TANG_Zhiheng | GenNoise | false | 8,944 | [
"MIT"
] | 0 | b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d | https://github.com/DDQXZcp/FYP_ProjectFile_TANG_Zhiheng/tree/b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d |
FocalLoss | # 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
... | Dong-JinKim/ActionCooccurrencePriors | FocalLoss | false | 7,970 | [
"MIT"
] | 27 | 110dbeecf4c25955b5b0160bd7d31d25c759ef21 | https://github.com/Dong-JinKim/ActionCooccurrencePriors/tree/110dbeecf4c25955b5b0160bd7d31d25c759ef21 |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Actis92/saint-lightning | GEGLU | false | 4,756 | [
"MIT"
] | 1 | 8f64fa0751fd7a36663f9e8b79bdea777905ea84 | https://github.com/Actis92/saint-lightning/tree/8f64fa0751fd7a36663f9e8b79bdea777905ea84 |
SE_layer_3d | import torch
import torch.nn as nn
import torch.multiprocessing
class SE_layer_3d(nn.Module):
def __init__(self, num_channels, reduction_ratio=2):
super(SE_layer_3d, self).__init__()
num_channels_reduced = num_channels // reduction_ratio
self.reduction_ratio = reduction_ratio
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | vinbigdata-medical/abdomen-phases | SE_layer_3d | false | 4,495 | [
"MIT"
] | 0 | 4adf5b8bf13aec85247d74e3cd3789c52cb88b92 | https://github.com/vinbigdata-medical/abdomen-phases/tree/4adf5b8bf13aec85247d74e3cd3789c52cb88b92 |
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
from torch._inductor.runtime.... | naver-ai/cgl_fairness | ResidualBlock | false | 7,335 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
PAM | import torch
from typing import Union
import torch.nn.functional as F
from typing import Tuple
from torch import nn
from typing import Dict
from typing import List
def strip_param_name(param_name: 'str') ->str:
"""Input an module's param name, return it's origin name with out parent modules' name
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 import triton_helpers
from torch._inductor.runtime.... | HAL-42/AlchemyCat | PAM | false | 17,354 | [
"Apache-2.0"
] | 8 | ca924755ff48e2ff74543bb0e446376eb2b1f150 | https://github.com/HAL-42/AlchemyCat/tree/ca924755ff48e2ff74543bb0e446376eb2b1f150 |
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.... | AviVarma/torchASN-Transformer | MultiHeadAttention | false | 107 | [
"MIT"
] | 0 | 55bccf4cdb099cd8e9ac99f5f87f989ce2add983 | https://github.com/AviVarma/torchASN-Transformer/tree/55bccf4cdb099cd8e9ac99f5f87f989ce2add983 |
folder | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class folder(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_map):
N, _, H, W = feature_map.size()
feature_map = F.unfold(feature_map, kernel_size=3, padding=1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | avinash-asink/AdelaiDet | folder | false | 1,501 | [
"BSD-2-Clause"
] | 0 | a8722579c8a724b02a36ef0f33a176ba282623fa | https://github.com/avinash-asink/AdelaiDet/tree/a8722579c8a724b02a36ef0f33a176ba282623fa |
SEBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = nn.Conv2d(in_channels=input_channels, out_channels=
internal_neurons, kernel_size=1, 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
assert_... | sysu-shey/ACNet | SEBlock | false | 16,523 | [
"MIT"
] | 767 | 6d967d3fff2d79a37f85799b78a21ffbd9001bd2 | https://github.com/sysu-shey/ACNet/tree/6d967d3fff2d79a37f85799b78a21ffbd9001bd2 |
RegularizationLoss | import torch
import torch.nn as nn
class RegularizationLoss(nn.Module):
def __init__(self, lambda_p: 'float', max_layers: 'int'):
super().__init__()
p_g = torch.zeros((max_layers,))
not_halted = 1.0
for k in range(max_layers):
p_g[k] = lambda_p * not_halted
... | 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... | ChenghaoMou/embeddings | RegularizationLoss | false | 7,883 | [
"MIT"
] | 12 | e63c2f2f4a688302de37bb8ccfd37a0170e2c374 | https://github.com/ChenghaoMou/embeddings/tree/e63c2f2f4a688302de37bb8ccfd37a0170e2c374 |
BertSelfAttention | # 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.... | codecaution/Hetu | BertSelfAttention | false | 1,741 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 |
StructuredAutoencoderNet | # 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 collections import OrderedDict
assert_size_stride = t... | CHuanSite/smautoPy | StructuredAutoencoderNet | false | 13,216 | [
"MIT"
] | 0 | 46c6b2088fc3c488870cee2ab88ac9f1ce779c0d | https://github.com/CHuanSite/smautoPy/tree/46c6b2088fc3c488870cee2ab88ac9f1ce779c0d |
MeanPoolConv | # 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.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | MeanPoolConv | false | 16,623 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
Balance_Theory | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class Balance_Theory(nn.Module):
"""The signed graph clustering model with balance theory, restricted to 2 hops for fair compari... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SherylHYX/SSSNET_Signed_Clustering | Balance_Theory | false | 17,922 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | # 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.... | almiliMSFT/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 14,815 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
MHSA | # 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.... | lzu-zhanghr/vision-transformer-zoo | MHSA | false | 10,475 | [
"MIT"
] | 0 | 2cc6e3551c39816acc95ade040bbf9bd115a6b03 | https://github.com/lzu-zhanghr/vision-transformer-zoo/tree/2cc6e3551c39816acc95ade040bbf9bd115a6b03 |
DQN | # 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 random
import torch.nn... | NeilWangziyu/torch_light | DQN | false | 5,684 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
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... | PogChamper/torch2trt | EQ | false | 14,180 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
SDNE_layer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class SDNE_layer(nn.Module):
def __init__(self, num_node, hidden_size1, hidden_size2, droput, alpha,
beta, nu1, nu2):
super(SDNE_layer, self).__init__()
self.num_node = num_node
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Brickser/cogdl | SDNE_layer | false | 2,275 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
MeanAct | # 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
... | jdasam/scDCC | MeanAct | false | 10,232 | [
"Apache-2.0"
] | 0 | 8ebaed766db5ad56021983ebc13e9a60b6c7b453 | https://github.com/jdasam/scDCC/tree/8ebaed766db5ad56021983ebc13e9a60b6c7b453 |
BiaffineAttention | import torch
import torch.utils.data.dataloader
from torch.nn import Parameter
import torch.nn as nn
import torch.nn
from torch.nn.parameter import Parameter
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dadmatech/DadmaTools | BiaffineAttention | false | 7,991 | [
"Apache-2.0"
] | 25 | c1b7add5c33544f69c1ba1c5250a5ea07caf9aa2 | https://github.com/Dadmatech/DadmaTools/tree/c1b7add5c33544f69c1ba1c5250a5ea07caf9aa2 |
AlphaEntropy | import torch
import torch.nn as nn
class AlphaEntropy(nn.Module):
def __init__(self):
super().__init__()
self.v_loss = nn.MSELoss()
def forward(self, props, v, pi, reward):
v_loss = self.v_loss(v, reward)
p_loss = -torch.mean(torch.sum(props * pi, 1))
return p_loss + ... | 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... | shinoyuki222/torch-light | AlphaEntropy | false | 16,423 | [
"MIT"
] | 310 | 4799805d9bcae82a9f12a574dcf9fdd838c92ee9 | https://github.com/shinoyuki222/torch-light/tree/4799805d9bcae82a9f12a574dcf9fdd838c92ee9 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | srlee-ai/claf | PositionwiseFeedForward | false | 10,884 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 |
MSELoss2d | import torch
import torch.nn as nn
class MSELoss2d(nn.Module):
def __init__(self, size_average=None, reduce=None, reduction='mean',
ignore_index=255):
super(MSELoss2d, self).__init__()
self.MSE = nn.MSELoss(size_average=size_average, reduce=reduce,
reduction=reduction)
de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | mimiliaogo/DACS | MSELoss2d | false | 16,092 | [
"MIT"
] | 59 | 9f13e32566c293de560df4848b23631d9e11cf32 | https://github.com/mimiliaogo/DACS/tree/9f13e32566c293de560df4848b23631d9e11cf32 |
VariableSoftmax | import torch
from torch import Tensor
from torch import nn
from typing import *
class VariableSoftmax(nn.Softmax):
"""Softmax with temperature"""
def __init__(self, temp: 'float'=1, dim: 'int'=-1):
super().__init__(dim=dim)
self.temp = temp
def forward(self, x: 'Tensor') ->Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | llayer/pytorch_inferno | VariableSoftmax | false | 3,936 | [
"Apache-2.0"
] | 0 | 922eba5e04e447126506512eb82adcd9ed1dab25 | https://github.com/llayer/pytorch_inferno/tree/922eba5e04e447126506512eb82adcd9ed1dab25 |
LocalNet | import torch
import torch.nn as nn
class LocalNet(nn.Module):
def forward(self, x_in):
"""Defines a double convolution
:param x_in: input convolutional features
:returns: convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(self.refpad(x_in)))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | deshwalmahesh/CURL---cpu-gpu | LocalNet | false | 3,421 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
BothContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ESCM-summarization/ESCM-summary-evaluation | BothContextGate | false | 9,117 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 |
BertLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Adelashl6/mask_transformers | BertLayerNorm | false | 4,787 | [
"MIT"
] | 1 | 2a2e4d1b40ae3ed546cb850d041af246806b63e7 | https://github.com/Adelashl6/mask_transformers/tree/2a2e4d1b40ae3ed546cb850d041af246806b63e7 |
GatedFusion | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class GatedFusion(nn.Module):
def __init__(self, hidden_size):
super(GatedFusion, self).__init__()
"""GatedFusion module"""
self.fc_z = nn.Linear(4 * hidden_size, hidden_size, bias=True)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
ass... | IBM/graph4nlp | GatedFusion | false | 8,349 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
SampaddingConv1D | # 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... | amoonfana/Knowledge_Distillation | SampaddingConv1D | false | 6,192 | [
"Apache-2.0"
] | 1 | 1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 | https://github.com/amoonfana/Knowledge_Distillation/tree/1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 |
TemporalAggregation_Mean | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class TemporalAggregation_Mean(nn.Module):
def __init__(self, cfg):
super(TemporalAggregation_Mean, self).__init__()
self.K = cfg.K
def forw... | 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
from math import sqrt as sqrt
from itertools import product as product
assert_size_stride = torch._C._dynamo.guards.as... | burhanmudassar/pytorch-action-detection | TemporalAggregation_Mean | false | 1,666 | [
"MIT"
] | 0 | 16afb9312248d73c0e2be56ac733e0a33040307e | https://github.com/burhanmudassar/pytorch-action-detection/tree/16afb9312248d73c0e2be56ac733e0a33040307e |
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