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 |
|---|---|---|---|---|---|---|---|---|---|---|
Inception_Temporal_Layer | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
self.padding = (kernel_size - 1) * dilation
super(CausalConv1d, self).__init__(in_channels, out_channels,
ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | WoodSugar/GSTNet | Inception_Temporal_Layer | false | 18,112 | [
"MIT"
] | 8 | 3c21cfc8a873d61336f257030a28fdee12dcee2f | https://github.com/WoodSugar/GSTNet/tree/3c21cfc8a873d61336f257030a28fdee12dcee2f |
SpatialGatherModule | # 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.... | CVIU-CSU/M2MRF-Lesion-Segmentation | SpatialGatherModule | false | 17,056 | [
"Apache-2.0"
] | 10 | 13af87927f4cdeca70e35d570edd1aec43b387b6 | https://github.com/CVIU-CSU/M2MRF-Lesion-Segmentation/tree/13af87927f4cdeca70e35d570edd1aec43b387b6 |
Selection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | aswanthkrishna/reinforced_scinet | Selection | false | 6,278 | [
"Apache-2.0"
] | 1 | b520f0c73bb1cdf0d0595f0df32372c96946d963 | https://github.com/aswanthkrishna/reinforced_scinet/tree/b520f0c73bb1cdf0d0595f0df32372c96946d963 |
CMlp | # 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... | LoveEachDay/towhee | CMlp | false | 11,655 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
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
import functools
impor... | AlphaLFC/mmdetection | SmoothL1Loss | false | 4,853 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
TransformerEncoderLayer | # 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.... | markovka17/efficient-dl-systems | TransformerEncoderLayer | false | 16,020 | [
"MIT"
] | 85 | 310d1471e72ba70a0892cf5c9653ade17f091be5 | https://github.com/markovka17/efficient-dl-systems/tree/310d1471e72ba70a0892cf5c9653ade17f091be5 |
HardAttn | # 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 ... | GoalballAnalysis/GUI | HardAttn | false | 2,313 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
ContextAttentionLayer | # 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.... | PaccMann/paccmann_predictor | ContextAttentionLayer | false | 8,691 | [
"MIT"
] | 19 | 58071311310c45c1efabb34a4003b96a1c58901a | https://github.com/PaccMann/paccmann_predictor/tree/58071311310c45c1efabb34a4003b96a1c58901a |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
def get_inputs():
return [torch.ran... | 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... | Artanic30/RentalPrediction | LayerNorm | false | 1,982 | [
"MIT"
] | 0 | 5804ab9b453d2a40bce2bb304c31efc98a803ed8 | https://github.com/Artanic30/RentalPrediction/tree/5804ab9b453d2a40bce2bb304c31efc98a803ed8 |
FocalLossV2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalSigmoidLossFunc(torch.autograd.Function):
"""
compute backward directly for better numeric stability
"""
@staticmethod
def forward(ctx, logits, label, alpha, gamma, reduction):
logits = logits.float()
co... | 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... | chizhu/pytorch-loss | FocalLossV2 | false | 6,436 | [
"MIT"
] | 1 | c8fbd78771f11a910b0b51ae3697c09761dd9696 | https://github.com/chizhu/pytorch-loss/tree/c8fbd78771f11a910b0b51ae3697c09761dd9696 |
RefTanhModule | import torch
class RefTanhModule(torch.nn.Module):
def forward(self, input):
return torch.tanh(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 libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | RaulMurillo/QPyTorch | RefTanhModule | false | 9,382 | [
"MIT"
] | 0 | b34c3a232ffdf387485b8a7e119a3729d066d5df | https://github.com/RaulMurillo/QPyTorch/tree/b34c3a232ffdf387485b8a7e119a3729d066d5df |
SSRLayer | # 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... | C3Imaging/SyntheticHeadPose | SSRLayer | false | 4,924 | [
"MIT"
] | 1 | b139aeda41ace2a07138705a4997d2ea65cb11a6 | https://github.com/C3Imaging/SyntheticHeadPose/tree/b139aeda41ace2a07138705a4997d2ea65cb11a6 |
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 torch import nn
import ... | QPC-database/multimodal-affinities | ContrastiveLoss | false | 5,723 | [
"Apache-2.0"
] | 1 | c3298e8db56a8b41110cc5681852f9f15d6deaa6 | https://github.com/QPC-database/multimodal-affinities/tree/c3298e8db56a8b41110cc5681852f9f15d6deaa6 |
CrossAttention | # 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.... | haophancs/TREQS | CrossAttention | false | 15,496 | [
"MIT"
] | 149 | 49e354ce2a08cf963ec139d99936020e0f80ced8 | https://github.com/haophancs/TREQS/tree/49e354ce2a08cf963ec139d99936020e0f80ced8 |
TorchPow | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ahangchen/torch2trt | TorchPow | false | 6,122 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
Lookahead | import torch
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
class Lookahead(nn.Module):
def __init__(self, n_features, context):
super(Lookahead, self).__init__()
assert context > 0
self.context = context
self.n_features = n_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.distributed
import torch.nn as nn
assert_size_stride = t... | faboyds/deepspeech.pytorch | Lookahead | false | 3,486 | [
"MIT"
] | 0 | d20f3510a3c556a07f5d662a91a63acffc26633b | https://github.com/faboyds/deepspeech.pytorch/tree/d20f3510a3c556a07f5d662a91a63acffc26633b |
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guard... | ZongqingHou/Pytorch_Retinaface | LandmarkHead | false | 3,097 | [
"MIT"
] | 0 | 6284b7158a0d9d3d4a2cc267a393c21863a1b938 | https://github.com/ZongqingHou/Pytorch_Retinaface/tree/6284b7158a0d9d3d4a2cc267a393c21863a1b938 |
SpatialSELayer3D | # 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... | YilinLiu97/AmygNet-Pytorch | SpatialSELayer3D | false | 18,150 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
PA | # 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... | qwopqwop200/Fast-Invertible-Rescaling-Net | PA | false | 7,521 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
CAE_ENC | # 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_... | positivevaib/semi-supervised-imagenet-classification | CAE_ENC | false | 4,188 | [
"MIT"
] | 0 | 4fb6427f5a72951c1b866a1ddbc2599811bb5770 | https://github.com/positivevaib/semi-supervised-imagenet-classification/tree/4fb6427f5a72951c1b866a1ddbc2599811bb5770 |
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
from torch import nn
a... | agikarasugi/Face-Mask-Invariant-End-to-End-Face-Recognition | FocalLoss | false | 6,088 | [
"MIT"
] | 1 | eb274ff98246c1bb8748bd8c8351d3494a87dfce | https://github.com/agikarasugi/Face-Mask-Invariant-End-to-End-Face-Recognition/tree/eb274ff98246c1bb8748bd8c8351d3494a87dfce |
SimpleBmmModule | # 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... | YaronBenAtar/glow | SimpleBmmModule | false | 14,652 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
ConvolutionModule | # 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 T... | huangruizhe/icefall | ConvolutionModule | false | 15,554 | [
"Apache-2.0"
] | 173 | ea8af0ee9af5169d93f8f389ffebbc27a1d9e82a | https://github.com/huangruizhe/icefall/tree/ea8af0ee9af5169d93f8f389ffebbc27a1d9e82a |
InnerProductLayer | import torch
import torch.nn as nn
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
"""
def __init__(self, num_feature_field, device):
"""
Args:
num_feature_field(int) :nu... | 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... | Ahren09/RecBole | InnerProductLayer | false | 1,924 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
InstanceNormLayer | # 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
import torch.fft
assert_size_stride = torch._C._dynamo.gu... | NejcHirci/material-addon | InstanceNormLayer | false | 17,770 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
CausalAttentionSortNet | import torch
from torch.nn import functional as F
from functools import partial
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 max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def expand_dim(t, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | blizda/sinkhorn-transformer | CausalAttentionSortNet | false | 9,867 | [
"MIT"
] | 0 | 4b626a40759010e4cb1752f22387fdbda438f37c | https://github.com/blizda/sinkhorn-transformer/tree/4b626a40759010e4cb1752f22387fdbda438f37c |
SqueezeExcite | # 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 ... | david1309/SynergyNet_bonseyes | SqueezeExcite | false | 3,387 | [
"MIT"
] | 0 | 9d675f6e0c78222e1fa55e6598c3d11aa5dc799b | https://github.com/david1309/SynergyNet_bonseyes/tree/9d675f6e0c78222e1fa55e6598c3d11aa5dc799b |
BertPooler | # 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... | Alwaysproblem/examples-1 | BertPooler | false | 1,788 | [
"MIT"
] | 0 | 9754fa63ed1931489a21ac1f5b299f945e369a5c | https://github.com/Alwaysproblem/examples-1/tree/9754fa63ed1931489a21ac1f5b299f945e369a5c |
ZonoDenseLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size... | david-shmailov/adversarial-robustness-toolbox | ZonoDenseLayer | false | 6,529 | [
"MIT"
] | 1 | ad8b94d3928abe218cd6ab2eed1c5c21f1d6e420 | https://github.com/david-shmailov/adversarial-robustness-toolbox/tree/ad8b94d3928abe218cd6ab2eed1c5c21f1d6e420 |
TxtNet | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class TxtNet(nn.Module):
def __init__(self, code_len, txt_feat_len):
super(TxtNet, self).__init__()
self.fc1 = nn.Linear(txt_feat_len, 4096)
self.fc2 = nn.Linear(4096, code_len)
self.alpha = 1.0
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zzs1994/DJsRH | TxtNet | false | 16,841 | [
"MIT"
] | 53 | 6041c2df810723dd0052e2e5b7c6bd33033f0f21 | https://github.com/zzs1994/DJsRH/tree/6041c2df810723dd0052e2e5b7c6bd33033f0f21 |
Tanh | from torch.nn import Module
import torch
class Tanh(Module):
"""Rectified Tanh, since we predict betwee 0 and 1"""
def __init__(self):
super().__init__()
self.params = []
def forward(self, x):
self.x = x
return 0.5 * (1 + x.tanh())
def backward(self, d_dx):
r... | 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
assert_size_stride = torch._C._dynamo.guards.assert... | LucaZampieri/DL | Tanh | false | 772 | [
"MIT"
] | 0 | e53ade2638ccc3ca368e15c8454845856776e719 | https://github.com/LucaZampieri/DL/tree/e53ade2638ccc3ca368e15c8454845856776e719 |
KnowledgeDistillationLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | edoardofantolino/MLDLproject4 | KnowledgeDistillationLoss | false | 12,333 | [
"MIT"
] | 0 | fed0cfd51f5984bbf21205a43ea43dc49f4d289a | https://github.com/edoardofantolino/MLDLproject4/tree/fed0cfd51f5984bbf21205a43ea43dc49f4d289a |
Swish | # 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
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_ptr... | khayliang/single_person_tracking | Swish | false | 3,828 | [
"MIT"
] | 0 | d93aae3742ba3c77f00b3917b182784f03b5d597 | https://github.com/khayliang/single_person_tracking/tree/d93aae3742ba3c77f00b3917b182784f03b5d597 |
VNMaxPool | import torch
import torch.nn as nn
import torch.utils.data
import torch
import torch.nn.parallel
class VNMaxPool(nn.Module):
def __init__(self, in_channels):
super(VNMaxPool, self).__init__()
self.map_to_dir = nn.Linear(in_channels, in_channels, bias=False)
def forward(self, x):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch
import torch.nn.paral... | Tiamat-Tech/vnn | VNMaxPool | false | 14,508 | [
"MIT"
] | 280 | f3197e210022b5f0015e0da6456adf66bd0cd73e | https://github.com/Tiamat-Tech/vnn/tree/f3197e210022b5f0015e0da6456adf66bd0cd73e |
BCE_LOSS | import math
import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch.nn
class BCE_LOSS(_Loss):
def __init__(self):
super().__init__()
self.bce_loss = torch.nn.BCEWithLogitsLoss()
def forward(self, input, label):
one_hot = torch.zeros_like(input)
C ... | 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.... | www516717402/EOD | BCE_LOSS | false | 10,945 | [
"Apache-2.0"
] | 0 | 89ee81a0cb5a5f64a8f788248e2bb3eccee7006d | https://github.com/www516717402/EOD/tree/89ee81a0cb5a5f64a8f788248e2bb3eccee7006d |
SELoss | import torch
from torch import Tensor
from torch import nn
class SELoss(nn.MSELoss):
def __init__(self):
super().__init__(reduction='none')
def forward(self, inputs: 'Tensor', target: 'Tensor') ->Tensor:
return super().forward(inputs, target).sum(1)
def get_inputs():
return [torch.rand... | 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... | bzrry/lightning-bolts | SELoss | false | 14,995 | [
"Apache-2.0"
] | 822 | bd392ad858039290c72c20cc3f10df39384e90b9 | https://github.com/bzrry/lightning-bolts/tree/bd392ad858039290c72c20cc3f10df39384e90b9 |
Model_CIFAR10 | # 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.... | Pluriscient/learn-to-learn | Model_CIFAR10 | false | 11,790 | [
"MIT"
] | 0 | 4aa0143522eb90f6439b83ed424d12b434cb344b | https://github.com/Pluriscient/learn-to-learn/tree/4aa0143522eb90f6439b83ed424d12b434cb344b |
AvgPoolShortening | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class AvgPoolShortening(Module):
"""
### Average pool shortening
This down-samples by a given factor with average pooling
"""
def __init__(self, k: 'int'):
... | 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 import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride... | techthiyanes/annotated_deep_learning_paper_implementations | AvgPoolShortening | false | 16,540 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
SimpleLogSoftmaxModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogSoftmaxModel(torch.nn.Module):
def __init__(self, dimension):
super(SimpleLogSoftmaxModel, self).__init__()
self.dimension = dimension
def forward(self, tensor):
return F.lo... | 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.jit
impor... | andreas-hommel/glow | SimpleLogSoftmaxModel | false | 3,340 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
LeakyReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | collector-m/LiDAR-MOS | LeakyReLU | false | 15,060 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
JsdCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class JsdCrossEntropy(nn.Module):
def __init__(self):
super(JsdCrossEntropy, self).__init__()
def forward(self, net_1_logits, net_2_logits):
net_1_probs = F.softmax(net_1_logits, dim=1)
net_2_probs = F.softmax(net_2_l... | 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... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | JsdCrossEntropy | false | 17,742 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
ArcBiaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.nn.init as init
assert... | LindaCY/fastNLP | ArcBiaffine | false | 17,619 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
VertexDirectEmbedder | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from... | av777x/detectron2 | VertexDirectEmbedder | false | 3,151 | [
"Apache-2.0"
] | 0 | c1794881d6d2fac6af0b3206937d32628677469c | https://github.com/av777x/detectron2/tree/c1794881d6d2fac6af0b3206937d32628677469c |
ClassHead | import torch
from torch import nn
import torch.nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
kernel_size=(1, 1), stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guard... | ZongqingHou/Pytorch_Retinaface | ClassHead | false | 3,074 | [
"MIT"
] | 0 | 6284b7158a0d9d3d4a2cc267a393c21863a1b938 | https://github.com/ZongqingHou/Pytorch_Retinaface/tree/6284b7158a0d9d3d4a2cc267a393c21863a1b938 |
MVCRegularizer | import torch
import torch.nn.parallel
import torch.utils.data
class MVCRegularizer(torch.nn.Module):
"""
penalize MVC with large absolute value and negative values
alpha * large_weight^2 + beta * (negative_weight)^2
"""
def __init__(self, alpha=1.0, beta=1.0, threshold=5.0):
super().__ini... | 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.parall... | KunalMGupta/deep_cage | MVCRegularizer | false | 13,958 | [
"MIT"
] | 123 | d8454c40d650911341b7f594af2fcefcf26f3d1d | https://github.com/KunalMGupta/deep_cage/tree/d8454c40d650911341b7f594af2fcefcf26f3d1d |
CELoss | # 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
... | ahmad4633/mmfashion | CELoss | false | 6,128 | [
"Apache-2.0"
] | 1 | ad2c911bf71bb95dce340a963e7f83c477a84824 | https://github.com/ahmad4633/mmfashion/tree/ad2c911bf71bb95dce340a963e7f83c477a84824 |
TVLoss | import torch
from torch import Tensor
import torch.utils.data
import torch.utils.data.dataset
import torch
import torch.nn as nn
import torch.utils.data.distributed
class TVLoss(nn.Module):
"""Regularization loss based on Li FeiFei."""
def __init__(self, weight: 'Tensor') ->None:
"""The weight inform... | 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 Tensor
import torch.utils.data
import torch.utils.data.dataset
import torch
import torch.nn as nn
import torch.utils.data.... | Tubbz-alt/SRGAN-PyTorch-2 | TVLoss | false | 5,936 | [
"Apache-2.0"
] | 1 | c1a01c99287a6212a3dc76ac17baafcf1c9f3013 | https://github.com/Tubbz-alt/SRGAN-PyTorch-2/tree/c1a01c99287a6212a3dc76ac17baafcf1c9f3013 |
ZeroConv1d | # 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 math as tl_math
from torch im... | batikim09/FloWaveNet | ZeroConv1d | false | 14,941 | [
"MIT"
] | 499 | 791f51aff530b2af4f9aa0d9fcb4af53d28a0997 | https://github.com/batikim09/FloWaveNet/tree/791f51aff530b2af4f9aa0d9fcb4af53d28a0997 |
GlobalAttention | # 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.... | csk7/CS550-NLP-McGill- | GlobalAttention | false | 9,968 | [
"MIT"
] | 0 | a6f295b88539015d8accdbd410357c42df7c4287 | https://github.com/csk7/CS550-NLP-McGill-/tree/a6f295b88539015d8accdbd410357c42df7c4287 |
DeConvNet64 | # 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_... | GloryyrolG/normalized-autoencoders | DeConvNet64 | false | 543 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
CorrelationAlignmentLoss | import torch
import torch.nn as nn
import torch.utils.data
class CorrelationAlignmentLoss(nn.Module):
"""The `Correlation Alignment Loss` in
`Deep CORAL: Correlation Alignment for Deep Domain Adaptation (ECCV 2016) <https://arxiv.org/pdf/1607.01719.pdf>`_.
Given source features :math:`f_S` and target fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | XianyuanLiu/Transfer-Learning-Library | CorrelationAlignmentLoss | false | 10,136 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
InvConv2d | # 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
from torch.nn import functional as F
assert_size_stride = t... | mbaddar1/glow-pytorch | InvConv2d | false | 7,184 | [
"MIT"
] | 1 | e07ca542ce4dd93ddf680c51eda25d1f9db252a1 | https://github.com/mbaddar1/glow-pytorch/tree/e07ca542ce4dd93ddf680c51eda25d1f9db252a1 |
LayerNorm | import torch
class LayerNorm(torch.nn.Module):
def __init__(self, dimensions, eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimensions))
self.beta = torch.nn.Parameter(torch.zeros(dimensions))
self.eps = eps
def forward(self, 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.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | AutuanLiu/LeetCode2019 | LayerNorm | false | 4,882 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 |
VGGBase | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This is used when we conve... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision
from torch... | dee-walia20/SSD-Implementation-using-Pytorch | VGGBase | false | 7,169 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
import torchvision.transforms.functional as F
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | CTPLab/IID_representation_learning | ModulatedConv2d | false | 5,213 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
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.... | beneisner/pytorch_geometric | Attention | false | 6,328 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
LowRankResidualMultiHeadAttention | # 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.... | bahducoup/factorized_training | LowRankResidualMultiHeadAttention | false | 12,163 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
SimpleConv2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConv2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, dilation=1, groups=1):
super(SimpleConv2dModule, self).__init__()
self.stride = stride
self.padding = padding
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | briancoutinho/glow | SimpleConv2dModule | false | 12,559 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
UniformDistributionLoss | # 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... | KevinMusgrave/pytorch-adapt | UniformDistributionLoss | false | 13,945 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
_BoundaryRefineModule | # 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... | sharanry/pytorch-semantic-segmentation | _BoundaryRefineModule | false | 12,971 | [
"MIT"
] | 0 | 47d637e3d5fcc1e2569203306c2fa5dca6f0e68a | https://github.com/sharanry/pytorch-semantic-segmentation/tree/47d637e3d5fcc1e2569203306c2fa5dca6f0e68a |
FCDiscriminator_Local | import torch
import torch.nn as nn
class FCDiscriminator_Local(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator_Local, self).__init__()
self.conv1 = nn.Conv2d(num_classes + 2048, ndf, kernel_size=4,
stride=2, padding=1)
self.conv2 = nn.Conv2d(ndf, ndf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | gabriel-tjio/ASH | FCDiscriminator_Local | false | 15,588 | [
"MIT"
] | 300 | 40ae044a7ca1809f91ba89671d223a96eda327da | https://github.com/gabriel-tjio/ASH/tree/40ae044a7ca1809f91ba89671d223a96eda327da |
FFB | # 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 as sqrt
from itertools import produc... | chickenshawama/CFEnet2 | FFB | false | 1,689 | [
"MIT"
] | 0 | f20dda7ad2923bed4af5d63e705b6849a48e73df | https://github.com/chickenshawama/CFEnet2/tree/f20dda7ad2923bed4af5d63e705b6849a48e73df |
PoseHead | import torch
import torch.nn as nn
class PoseHead(nn.Module):
def __init__(self, input_dim=256, hidden_dim=128):
super(PoseHead, self).__init__()
self.conv1_pose = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2_pose = nn.Conv2d(hidden_dim, 6, 3, padding=1)
self.relu = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aliyun/dro-sfm | PoseHead | false | 14,817 | [
"MIT"
] | 147 | 8707e2e0ef799d7d47418a018060f503ef449fe3 | https://github.com/aliyun/dro-sfm/tree/8707e2e0ef799d7d47418a018060f503ef449fe3 |
ScaledDotProductAttention | # 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.... | BlackNoodle/TUCORE-GCN | ScaledDotProductAttention | false | 7,807 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
Aggregate | import torch
from torch import nn as nn
class Aggregate(nn.Module):
"""Pooling layer based on sum or average with optional masking.
Args:
axis (int): axis along which pooling is done.
mean (bool, optional): if True, use average instead for sum pooling.
keepdim (bool, optional): whethe... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | AlexanderDKazakov/schnetpack | Aggregate | false | 13 | [
"MIT"
] | 0 | 97b82469d977981b500e439a6c93696d8dac8a3f | https://github.com/AlexanderDKazakov/schnetpack/tree/97b82469d977981b500e439a6c93696d8dac8a3f |
RMSE | import torch
import torch.nn as nn
class RMSE(nn.Module):
def __init__(self):
super(RMSE, self).__init__()
def forward(self, outputs, target, *args):
val_pixels = (target > 0).float() * (outputs > 0).float()
err = (target * val_pixels - outputs * val_pixels) ** 2
loss = torch... | 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_... | anglixjtu/MSG_CHN_WACV20 | RMSE | false | 14,854 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
DenseResidualBlock | # 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 ... | DaikiSannoXC/simple-cnaps | DenseResidualBlock | false | 13,546 | [
"MIT"
] | 62 | be35c4522b180eaae8278633b1c6ca7e5bb56ebb | https://github.com/DaikiSannoXC/simple-cnaps/tree/be35c4522b180eaae8278633b1c6ca7e5bb56ebb |
GEGLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Lawliet19189/squad-1 | GEGLU | false | 11,635 | [
"MIT"
] | 0 | 75531054d74e20838d8acff81749f335973b9ae3 | https://github.com/Lawliet19189/squad-1/tree/75531054d74e20838d8acff81749f335973b9ae3 |
ConvTranspose | # 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 typing import Union
import torch.nn as nn
from typing import Tuple
assert_s... | Latterlig96/DCUnet | ConvTranspose | false | 8,457 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
PosLinear | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
gain = 1 / x.size(1)
return nn.functional.linear(x, torch.nn.functional.softplus(self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | KelvinKan/CP-Flow | PosLinear | false | 13,939 | [
"MIT"
] | 64 | d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 | https://github.com/KelvinKan/CP-Flow/tree/d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 |
Invertible1x1Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
from torch.autograd import Variable
import torch... | eba472/fastPitchPyTorch | Invertible1x1Conv | false | 10,041 | [
"BSD-3-Clause"
] | 0 | 0f946c05539102e6868f72f5bf2c461d9711e7d7 | https://github.com/eba472/fastPitchPyTorch/tree/0f946c05539102e6868f72f5bf2c461d9711e7d7 |
SAP | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | albertvillanova/s3prl | SAP | false | 6,167 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
LipNormLinear | # 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 math as tl_math
import torch.... | hologerry/residual-flows | LipNormLinear | false | 10,304 | [
"MIT"
] | 0 | 33a3639150490279c2e13238dd6244b80c52adf7 | https://github.com/hologerry/residual-flows/tree/33a3639150490279c2e13238dd6244b80c52adf7 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
"""Custom weight init for Conv2D and Linear layers."""
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
m.bias.data.fill_(0.0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LQNew/LWDRL | Actor | false | 8,697 | [
"MIT"
] | 11 | 0e4fab077a0cfbd27590b840557f4fda033c74ff | https://github.com/LQNew/LWDRL/tree/0e4fab077a0cfbd27590b840557f4fda033c74ff |
ConvNet2FC | import torch
import torch.nn as nn
def spectral_norm(module, init=True, std=1, bound=False):
if init:
nn.init.normal_(module.weight, 0, std)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.zero_()
SpectralNorm.apply(module, 'weight', bound=bound)
return module
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | GloryyrolG/normalized-autoencoders | ConvNet2FC | false | 519 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
DotAtte | # 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.... | FengZiYjun/fastNLP | DotAtte | false | 5,160 | [
"Apache-2.0"
] | 1 | 3ae73ab0a05d1ceef4a5181516891a8057d7f719 | https://github.com/FengZiYjun/fastNLP/tree/3ae73ab0a05d1ceef4a5181516891a8057d7f719 |
Forward_Grad | # 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... | AmazingAng/pytorch-tvnet | Forward_Grad | false | 7,661 | [
"MIT"
] | 12 | e880d3ce15f55e5d9a11b423cfd1e0461de4fedb | https://github.com/AmazingAng/pytorch-tvnet/tree/e880d3ce15f55e5d9a11b423cfd1e0461de4fedb |
DPRNNCell | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | romovpa/opacus | DPRNNCell | false | 10,857 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 |
HeatmapLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.m... | ahmedelmahy/HRNet-Bottom-Up-Pose-Estimation | HeatmapLoss | false | 14,757 | [
"MIT"
] | 129 | cf5831249999f0b307d5aa948ebdcdef981ba68f | https://github.com/ahmedelmahy/HRNet-Bottom-Up-Pose-Estimation/tree/cf5831249999f0b307d5aa948ebdcdef981ba68f |
ModReLU | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def magnitude(input):
if input.ndimension() == 4:
return (input[:, :, :, 0] ** 2 + input[:, :, :, 1] ** 2) ** 0.5
elif input.ndimension() == 5:
return (input[:, :, :, :, 0] ** 2 + input[:, :, :, :, 1] ** 2) ** 0.5
... | 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 torch.nn.parameter import Parameter
assert_size_stri... | wizofe/urus-mri-recon | ModReLU | false | 4,543 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
TensorMin | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/kedex | TensorMin | false | 9,687 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
SourceContextGate | # 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 ... | BradLin0819/kg2text | SourceContextGate | false | 13,413 | [
"Apache-2.0"
] | 86 | e586eb2027c0d85db9826cbe1d9e14f2d26fc93f | https://github.com/BradLin0819/kg2text/tree/e586eb2027c0d85db9826cbe1d9e14f2d26fc93f |
SelfAttentionBatch | import torch
from torch import nn
import torch.nn.functional as F
class SelfAttentionBatch(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super(SelfAttentionBatch, self).__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | shubaoyu/CRSLab | SelfAttentionBatch | false | 10,819 | [
"MIT"
] | 0 | a05730e8b2c03df278587be34923fa818945d4c4 | https://github.com/shubaoyu/CRSLab/tree/a05730e8b2c03df278587be34923fa818945d4c4 |
VAE | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.init as init
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | aasensio/neural_hinode | VAE | false | 3,028 | [
"MIT"
] | 0 | 63ec076d920f82343618ce67669c73a3b5209957 | https://github.com/aasensio/neural_hinode/tree/63ec076d920f82343618ce67669c73a3b5209957 |
mlp_3layer | # 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_... | mnmueller/auto_LiRPA | mlp_3layer | false | 7,261 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
ImgLayerNorm | # 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.nn
import torch.utils.data
assert_size... | CrhistyanSilva/localbitsback | ImgLayerNorm | false | 13,530 | [
"MIT"
] | 100 | bdf66b41b2120c5b35edac4e4efda0fda3f2db4d | https://github.com/CrhistyanSilva/localbitsback/tree/bdf66b41b2120c5b35edac4e4efda0fda3f2db4d |
CAModule | import torch
from torch import nn
class CAModule(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
code reference:
https://github.com/kobiso/CBAM-keras/blob/master/models/attention_module.py
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DavidChenL/Chexpert | CAModule | false | 13,581 | [
"Apache-2.0"
] | 202 | 0300057d3a51301cff35a65f79729436678b4a79 | https://github.com/DavidChenL/Chexpert/tree/0300057d3a51301cff35a65f79729436678b4a79 |
SigmoidDiceLoss | # 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... | azxj/BRRNet | SigmoidDiceLoss | false | 6,318 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
TransformerEncoderFeedForward | # 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.... | RoySadaka/lpd | TransformerEncoderFeedForward | false | 17,858 | [
"MIT"
] | 4 | 921454d9730d8228f4b0ca5349b0558ebd123c65 | https://github.com/RoySadaka/lpd/tree/921454d9730d8228f4b0ca5349b0558ebd123c65 |
AdaptiveInstanceNorm | # 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 ... | bladesaber/mmgeneration | AdaptiveInstanceNorm | false | 1,573 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
ResBlock | import torch
import torch.nn as nn
from torch.nn import functional as F
class ResBlock(nn.Module):
"""Residual block with bilinear upsampling/downsampling.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
mode (str): Upsampling/do... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | abhishekm47/GFPGAN | ResBlock | false | 12,044 | [
"BSD-3-Clause"
] | 0 | 39d063749433b38d98c75740b052934ae8bc80f6 | https://github.com/abhishekm47/GFPGAN/tree/39d063749433b38d98c75740b052934ae8bc80f6 |
F_fully_convolutional | # 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... | ramonpeter/LaSeR | F_fully_convolutional | false | 7,548 | [
"MIT"
] | 1 | 28daa6876256501ed0d3e84a4ddfedc7892bd528 | https://github.com/ramonpeter/LaSeR/tree/28daa6876256501ed0d3e84a4ddfedc7892bd528 |
Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | suyukun666/UFO | Loss | false | 16,543 | [
"MIT"
] | 122 | e57016948b03cd2f75155d2958cea69b6e4b56f8 | https://github.com/suyukun666/UFO/tree/e57016948b03cd2f75155d2958cea69b6e4b56f8 |
SimpleNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
class SimpleNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(SimpleNet, self).__init__()
self.s_dim = s_dim
self.a_dim = a_dim
self.a1 = nn.Linear(s_dim, 100)
self.mu ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SpencerLo-CMU/pytorch-rl-suite | SimpleNet | false | 5,847 | [
"MIT"
] | 1 | 52b215f38cbb4c39a0ccfff48ab8262b1c9ef4a0 | https://github.com/SpencerLo-CMU/pytorch-rl-suite/tree/52b215f38cbb4c39a0ccfff48ab8262b1c9ef4a0 |
PositionEmbedding2D | # 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 logging
import torch.n... | hikopensource/DAVAR-Lab-OCR | PositionEmbedding2D | false | 15,526 | [
"Apache-2.0"
] | 387 | c65285f6668864cca7a12770ae4c8d083ea1cf1b | https://github.com/hikopensource/DAVAR-Lab-OCR/tree/c65285f6668864cca7a12770ae4c8d083ea1cf1b |
BCE_disc_sm_v6 | # AOT ID: ['2_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... | Sampson-Lee/SIB-Net | BCE_disc_sm_v6 | false | 2,811 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
ShakeResNet | # 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 math
from torch import... | pemcconnell-anyvision/fast-autoaugment | ShakeResNet | false | 12,874 | [
"MIT"
] | 0 | 047cf4bb9ffb85d0e8266a425347cdfe99d16902 | https://github.com/pemcconnell-anyvision/fast-autoaugment/tree/047cf4bb9ffb85d0e8266a425347cdfe99d16902 |
LocalMultiHeadAttention | # 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.... | debasish-mihup/EfficientConformer | LocalMultiHeadAttention | false | 10,352 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.init
import torch.utils.data
import torch.utils.data.distributed
def cosine_sim(im, s):
return im.mm(s.t())
class ContrastiveLoss(nn.Module):
def __init__(self, margin=0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
... | 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.init
import torch.utils.data
import torch.utils.dat... | nfyfamr/ActionEstimation | ContrastiveLoss | false | 10,576 | [
"MIT"
] | 0 | 8f18dba49d9558b28a277ea82c70fb4e3425bbbb | https://github.com/nfyfamr/ActionEstimation/tree/8f18dba49d9558b28a277ea82c70fb4e3425bbbb |
PatchEmbedding | import torch
import torch.nn as nn
def bchw_to_bhwc(input: 'torch.Tensor') ->torch.Tensor:
"""
Permutes a tensor to the shape [batch size, height, width, channels]
:param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
:return: (torch.Tensor) Output tensor of the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChristophReich1996/Swin-Transformer-V2 | PatchEmbedding | false | 7,923 | [
"MIT"
] | 43 | d71c1b412cd0fe13dc2557ad090cf0f027e54d47 | https://github.com/ChristophReich1996/Swin-Transformer-V2/tree/d71c1b412cd0fe13dc2557ad090cf0f027e54d47 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.