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 |
|---|---|---|---|---|---|---|---|---|---|---|
MyMul | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class MyMul(nn.Module):
def __init__(self, size):
super(MyMul, self).__init__()
self.weight = nn.Parameter(torch.rand(1))
def forward(self, x):
out = x * torch.abs(self.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.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
import torch.optim
import t... | JurijsNazarovs/bayesian_nn | MyMul | false | 17,519 | [
"MIT"
] | 6 | 936bf55e0a1e620504d5159c100a74493bd16399 | https://github.com/JurijsNazarovs/bayesian_nn/tree/936bf55e0a1e620504d5159c100a74493bd16399 |
PitchShift | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | shaun95/StarGANv2-VC | PitchShift | false | 16,402 | [
"MIT"
] | 116 | ed20538971a03d699351a349a3631767333baeb7 | https://github.com/shaun95/StarGANv2-VC/tree/ed20538971a03d699351a349a3631767333baeb7 |
OfstMapL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | StannisZhou/FFB6D | OfstMapL1Loss | false | 11,898 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
SineActivation | # 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... | jaredfeng-ca/Time2Vec-PyTorch | SineActivation | false | 3,706 | [
"MIT"
] | 0 | b42205f6721f5a6faf16134e604af28476490d0a | https://github.com/jaredfeng-ca/Time2Vec-PyTorch/tree/b42205f6721f5a6faf16134e604af28476490d0a |
InvGridSamplerNumerator | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
import torch.utils.data
def ravel_multi_index(indices, shape):
indices_ravel = indices[0]
for i in range(1, len(indices)):
indices_ravel = indices_ravel * shape[i] + indices[i]
return indices_ravel
def add_repea... | 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 numpy as np
fro... | EasyTry/coordinate_based_inpainting | InvGridSamplerNumerator | false | 8,054 | [
"MIT"
] | 13 | cbe0e3a58c8cb2054f0536a56f57264fd9967d63 | https://github.com/EasyTry/coordinate_based_inpainting/tree/cbe0e3a58c8cb2054f0536a56f57264fd9967d63 |
TripletGCN | from torch.nn import Module
import math
import torch
from torch.nn.modules import Module
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
Z = f(X, A) = softmax(A` * ReLU(A` * X * W0)* ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Brain03Yao/M2TGCN | TripletGCN | false | 17,024 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | axiserr/Hetu | BertSelfAttention | false | 14,937 | [
"Apache-2.0"
] | 82 | 0052f727488db0570d6b37f63549b43b0920bc29 | https://github.com/axiserr/Hetu/tree/0052f727488db0570d6b37f63549b43b0920bc29 |
VirtualBatchNorm1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils
import torch.utils.data
from tor... | Silent-Zebra/JEM | VirtualBatchNorm1d | false | 17,937 | [
"Apache-2.0"
] | 6 | 33440aff8429d9a24a8ba858d0209f4b48be8e05 | https://github.com/Silent-Zebra/JEM/tree/33440aff8429d9a24a8ba858d0209f4b48be8e05 |
Embeddings | import torch
from torch import nn
import torch._utils
class Embeddings(nn.Module):
def __init__(self, input_dim, embed_dim, cube_size, patch_size, dropout):
super().__init__()
self.n_patches = int(cube_size[0] * cube_size[1] * cube_size[2] / (
patch_size * patch_size * patch_size))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch._utils
assert_size_stride = torch._C._dynamo.g... | ilcessadecalcular/segmentation | Embeddings | false | 10,584 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
lovasz_hinge | import torch
import torch.nn.parallel
import torch.utils.data
from torchvision.transforms import functional as F
import torch.nn.functional as F
from torch.autograd import Variable
def flatten_binary_scores(scores, labels, ignore=255):
"""
Flattens predictions in the batch (binary case)
Remove labels equa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.utils.data
from torchvision.transforms import functional as F
import torch.nn.functional as F
from tor... | clovaai/ext_portrait_segmentation | lovasz_hinge | false | 15,067 | [
"MIT"
] | 227 | 9bc1bada1cb7bd17a3a80a2964980f4b4befef5b | https://github.com/clovaai/ext_portrait_segmentation/tree/9bc1bada1cb7bd17a3a80a2964980f4b4befef5b |
GeneralizedMeanPooling | import torch
from torchvision.transforms import *
from torch import nn
class GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision.transforms ... | ZoRoronoa/Camera-Aware-Proxy | GeneralizedMeanPooling | false | 2,998 | [
"Apache-2.0"
] | 0 | 352f900bbae330f18c2bfe2b3f2516fb4e31adea | https://github.com/ZoRoronoa/Camera-Aware-Proxy/tree/352f900bbae330f18c2bfe2b3f2516fb4e31adea |
BinaryFocalLossWithLogits | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | adi1999/kornia | BinaryFocalLossWithLogits | false | 12,058 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | bb476a36e2725d687d1879b5a0d877c1ba860c25 | https://github.com/adi1999/kornia/tree/bb476a36e2725d687d1879b5a0d877c1ba860c25 |
MmRefsHead | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(LayerNorm, self).__init__()
self.weight = nn.Paramet... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | MILVLG/rosita | MmRefsHead | false | 8,793 | [
"Apache-2.0"
] | 32 | 13f7e68350a64b4b5b2c44b9fa4e7448bbe7420c | https://github.com/MILVLG/rosita/tree/13f7e68350a64b4b5b2c44b9fa4e7448bbe7420c |
CrossAttentionSublayer | # 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.... | Nickeilf/pysimt | CrossAttentionSublayer | false | 9,515 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
RMSNorm | import torch
import torch.nn as nn
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-08):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | poliver269/latent-diffusion | RMSNorm | false | 12,901 | [
"MIT"
] | 0 | 08e7c987ad423e3f93125b49980c36302ffe3d82 | https://github.com/poliver269/latent-diffusion/tree/08e7c987ad423e3f93125b49980c36302ffe3d82 |
BCELoss | import torch
import torch.nn as nn
class BCELoss(nn.BCELoss):
def __init__(self, **kwargs):
super(BCELoss, self).__init__(**kwargs)
def forward(self, input, target):
input = input.squeeze(1)
target = target.float()
return super(BCELoss, self).forward(input, target)
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | azxj/BRRNet | BCELoss | false | 6,296 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
SEModule | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class FastAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | GuillaumeAI/gia-labeling-ImageNet21K | SEModule | false | 17,323 | [
"MIT"
] | 4 | 825ff49f1558f848fc8a798e2e393b708e75bb0e | https://github.com/GuillaumeAI/gia-labeling-ImageNet21K/tree/825ff49f1558f848fc8a798e2e393b708e75bb0e |
UpsampleNet | # 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 ... | SolomidHero/EA-SVC | UpsampleNet | false | 14,434 | [
"MIT"
] | 88 | 23a0a9d9c0e9670dd7c777d56b00883d84c23237 | https://github.com/SolomidHero/EA-SVC/tree/23a0a9d9c0e9670dd7c777d56b00883d84c23237 |
GroupedLinearLayer | # 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.checkpoint
assert_size_stride = torch._C... | Clemens123/transformers | GroupedLinearLayer | false | 11,498 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
SSGConv | # 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_... | EdisonLeeeee/Graphgallery | SSGConv | false | 5,104 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
ClassicMixtureDensityModule | import torch
from torch import nn
class ClassicMixtureDensityModule(nn.Module):
def __init__(self, dim_input, dim_output, num_components):
super(ClassicMixtureDensityModule, self).__init__()
self.dim_input = dim_input
self.dim_output = dim_output
self.M = num_components
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.... | Woodenonez/MultimodalMotionPred_SamplingWTACGF_Pytorch | ClassicMixtureDensityModule | false | 1,228 | [
"MIT"
] | 0 | 2be4f8aaaebb9ec80b29d4ff86146010a0192573 | https://github.com/Woodenonez/MultimodalMotionPred_SamplingWTACGF_Pytorch/tree/2be4f8aaaebb9ec80b29d4ff86146010a0192573 |
SolutionModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class SolutionModel(nn.Module):
def __init__(self, input_size, output_size):
super(SolutionModel, self).__init__()
self.input_size = input_size
self.hidden_size = 32
self.linear1 = nn.Linear(input_size, 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 import triton_helpers
import torch.nn as nn
assert_... | VVKot/mlinseconds-vote-prediction | SolutionModel | false | 2,938 | [
"MIT"
] | 0 | c869ae428fb8d5e83f0a47468722da968aed28c6 | https://github.com/VVKot/mlinseconds-vote-prediction/tree/c869ae428fb8d5e83f0a47468722da968aed28c6 |
FasterBlock | import torch
import torch.nn as nn
def get_operator_from_cfg(operator_cfg):
operator_cfg_copy = operator_cfg.copy()
construct_str = 'nn.'
construct_str += operator_cfg_copy.pop('type') + '('
for k, v in operator_cfg_copy.items():
construct_str += k + '=' + str(v) + ','
construct_str += ')'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | becauseofAI/DemoHub | FasterBlock | false | 3,202 | [
"Apache-2.0"
] | 0 | 2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da | https://github.com/becauseofAI/DemoHub/tree/2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da |
RSoftmax | # 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
... | AnonSubmission6150/submission6150 | RSoftmax | false | 8,989 | [
"Apache-2.0"
] | 0 | 571633d9a12b4fd7a9546947787fc068966dab04 | https://github.com/AnonSubmission6150/submission6150/tree/571633d9a12b4fd7a9546947787fc068966dab04 |
TransformerDecoderLayer | # 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.... | salmon7ish/Video-Captioning | TransformerDecoderLayer | false | 4,393 | [
"MIT"
] | 0 | 08359b1824195a7f5eac5b58982efd19ebc6db01 | https://github.com/salmon7ish/Video-Captioning/tree/08359b1824195a7f5eac5b58982efd19ebc6db01 |
Actor | # 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.... | gntoni/pytorch-ddpg-naf | Actor | false | 12,471 | [
"MIT"
] | 0 | d208d0c0c38a9d2d2041f1e7e95695359eba430e | https://github.com/gntoni/pytorch-ddpg-naf/tree/d208d0c0c38a9d2d2041f1e7e95695359eba430e |
Entmax15 | # 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.autograd import F... | gitlost-murali/awesome-align | Entmax15 | false | 3,545 | [
"BSD-3-Clause"
] | 0 | 39fb45ca85a98e005447bddb52c48e65ce7d399b | https://github.com/gitlost-murali/awesome-align/tree/39fb45ca85a98e005447bddb52c48e65ce7d399b |
L2Norm | import torch
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | AlessandroRigoli/project_vg | L2Norm | false | 11,168 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 |
_MLP_B | import torch
import torch.nn as nn
class _MLP_B(nn.Module):
"""MLP that only use age gender MMSE"""
def __init__(self, in_size, drop_rate, fil_num):
super(_MLP_B, self).__init__()
self.fc1 = nn.Linear(in_size, fil_num)
self.fc2 = nn.Linear(fil_num, 2)
self.do1 = nn.Dropout(dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | colorfulbrain/brain2020 | _MLP_B | false | 15,072 | [
"MIT"
] | 91 | 1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 | https://github.com/colorfulbrain/brain2020/tree/1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 |
AttModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttModel(nn.Module):
def __init__(self, din, hidden_dim, dout):
super(AttModel, self).__init__()
self.fcv = nn.Linear(din, hidden_dim)
self.fck = nn.Linear(din, hidden_dim)
self.fcq = nn.Linear(din, hidden_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HuangHaoyu1997/pytorch_DGN | AttModel | false | 13,790 | [
"MIT"
] | 48 | f1b1a157a9b1678f9238f64458f44412b796d00e | https://github.com/HuangHaoyu1997/pytorch_DGN/tree/f1b1a157a9b1678f9238f64458f44412b796d00e |
GeM | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06, p_trainable=True)... | 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
from to... | h8c2/kaggle-landmark-recognition-2020-1st-place | GeM | false | 10,167 | [
"MIT"
] | 0 | 3285b6c9548d100b14800ea3927f5974b25facd9 | https://github.com/h8c2/kaggle-landmark-recognition-2020-1st-place/tree/3285b6c9548d100b14800ea3927f5974b25facd9 |
ReDynamicWeightsCat33 | import math
import torch
import torch.utils.data
from torch import nn
from torch.nn.modules.utils import _pair
class DeformConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, deformable_groups=1, bias=False):
assert not bias
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lzrobots/dgmn | ReDynamicWeightsCat33 | false | 16,012 | [
"MIT"
] | 54 | 515476b5c6a07dcc3b7a4d2243c541377624bb33 | https://github.com/lzrobots/dgmn/tree/515476b5c6a07dcc3b7a4d2243c541377624bb33 |
GMM_Module | import math
import torch
import torch.nn as nn
import torch.utils.data
class GMM_Module(nn.Module):
"""
GMM Module
"""
def __init__(self, out_channel_M, k):
super(GMM_Module, self).__init__()
self.conv1 = nn.Conv2d(int(out_channel_M), k * out_channel_M,
kernel_size=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
import math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | GMM_Module | false | 4,525 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
Mul | # 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 | Mul | false | 14,197 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
SimpleLogModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogModule(torch.nn.Module):
def __init__(self, *dimensions):
super(SimpleLogModule, self).__init__()
def forward(self, a):
b = torch.log(a)
return torch.log(b)
def get_inputs():
return [torch.rand([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.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | briancoutinho/glow | SimpleLogModule | false | 12,586 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SquaredReLU | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SquaredReLU(Module):
"""
## Squared ReLU activation
$$y = {\\max(x, 0)}^2$$
Squared ReLU is used as the activation function in the
[position wise feedforw... | 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.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.... | techthiyanes/annotated_deep_learning_paper_implementations | SquaredReLU | false | 16,559 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
FcCat | # 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... | zwh930712/densenet.pytorch | FcCat | false | 16,834 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | eric-yoo/HairNet | Net | false | 12,428 | [
"MIT"
] | 0 | 15725328709f3f0e63d122914f8e55d18c4fa1fa | https://github.com/eric-yoo/HairNet/tree/15725328709f3f0e63d122914f8e55d18c4fa1fa |
Gdn | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.utils
class GdnFunction(Function):
@staticmethod
def forward(ctx, x, gamma, beta):
ctx.save_for_backward(x, gamma, beta)
n, c, h, w = list(x.size())
tx = x.permute(0, 2, 3, 1).contiguous()
tx =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | adynmiles/DARTS-FQA | Gdn | false | 6,093 | [
"MIT"
] | 1 | a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f | https://github.com/adynmiles/DARTS-FQA/tree/a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f |
BertOutput | # 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
imp... | bamf-health/MONAI | BertOutput | false | 1,525 | [
"Apache-2.0"
] | 0 | 6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 | https://github.com/bamf-health/MONAI/tree/6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 |
TinyCnn | import torch
import torch.nn as nn
class TinyCnn(nn.Module):
def __init__(self, feature_extraction=False):
super().__init__()
self.feature_extraction = feature_extraction
self.conv1 = nn.Conv2d(3, 3, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 2)
if not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Europium248/captum | TinyCnn | false | 438 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
NoiseInjection | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jeromepl/style-based-gan-pytorch | NoiseInjection | false | 10,348 | [
"MIT"
] | 0 | 97c13e54316dc57a7cb44c0cb910c29aaed11738 | https://github.com/jeromepl/style-based-gan-pytorch/tree/97c13e54316dc57a7cb44c0cb910c29aaed11738 |
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.triton_helpers import libdevice
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.g... | StannisZhou/FFB6D | CosLoss | false | 11,896 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
ConvNorm | # 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert... | AppleHolic/FastSpeech2 | ConvNorm | false | 16,932 | [
"MIT"
] | 8 | 8f6969edd0c86c05b1dd70a0b7841bd86505455e | https://github.com/AppleHolic/FastSpeech2/tree/8f6969edd0c86c05b1dd70a0b7841bd86505455e |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, weight=1.0):
super(TVLoss, self).__init__()
self.weight = weight
self.l1 = nn.L1Loss(reduction='mean')
def forward(self, out, gt):
grad_out_x = out[:, :, :, 1:] - out[:, :, :, :-1]
grad_out... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | myeldib/Simple-SR | TVLoss | false | 12,808 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
PEG | import torch
from torch import nn
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PEG(nn.Module):
def __init__(self, dim, kernel_size=3):
super().__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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | idolumbantobing/vit-pytorch | PEG | false | 15,576 | [
"MIT"
] | 9,373 | eb70d8dca041cc387b3e1f72d965d8814eeab29a | https://github.com/idolumbantobing/vit-pytorch/tree/eb70d8dca041cc387b3e1f72d965d8814eeab29a |
MyLeakyReLU | import torch
import torch.nn as nn
class MyLeakyReLU(nn.Module):
def __init__(self, negative_slope=0.01):
super(MyLeakyReLU, self).__init__()
self.negative_slope = negative_slope
def forward(self, x):
return torch.clamp(x, min=0.0) + torch.clamp(x, max=0.0
) * self.negati... | 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... | dsarrut/gaga | MyLeakyReLU | false | 6,608 | [
"Apache-2.0"
] | 1 | 4b34210074f8f82acb12e0ffb38858e83c319dc3 | https://github.com/dsarrut/gaga/tree/4b34210074f8f82acb12e0ffb38858e83c319dc3 |
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
import triton
import 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... | zsl24/voice-activity-detection | Attention | false | 16,827 | [
"MIT"
] | 74 | a034be23c6283121c6b72e778c6ff6711045cbe3 | https://github.com/zsl24/voice-activity-detection/tree/a034be23c6283121c6b72e778c6ff6711045cbe3 |
SquareActivation | # 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... | Ergodice/PWLU | SquareActivation | false | 8,997 | [
"MIT"
] | 0 | 8e714cff4245b9282fe6b9420ffbab8178ba456c | https://github.com/Ergodice/PWLU/tree/8e714cff4245b9282fe6b9420ffbab8178ba456c |
MyLoss | import torch
import torch.nn as nn
class MyLoss(nn.Module):
def __init__(self):
super(MyLoss, self).__init__()
None
self.reduce_var = True
pass
"""
weights has shape (n), multiply loss of point i with weights[i]
"""
def forward(self, outputs, y, weights, calculate_add... | 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... | abrar-fahim/ann-benchmarks | MyLoss | false | 12,048 | [
"MIT"
] | 0 | e5493ddda333bf6a930415566d4f1c697b439aca | https://github.com/abrar-fahim/ann-benchmarks/tree/e5493ddda333bf6a930415566d4f1c697b439aca |
PolicyNetwork | # 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.... | linklab/link_rl_book_codes | PolicyNetwork | false | 10,420 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 |
LossAttentionLayer | # 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.... | apardyl/ProtoPNet | LossAttentionLayer | false | 6,223 | [
"MIT"
] | 1 | b2bbd7284bfc84a37385c0e975408c68cdf64205 | https://github.com/apardyl/ProtoPNet/tree/b2bbd7284bfc84a37385c0e975408c68cdf64205 |
Attn | # 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.... | dhpollack/mgc | Attn | false | 1,837 | [
"MIT"
] | 0 | ed1b8fb512f0b42cb8121a2809def65f232dc154 | https://github.com/dhpollack/mgc/tree/ed1b8fb512f0b42cb8121a2809def65f232dc154 |
Deconvolution | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Deconvolution(nn.Module):
def __init__(self, C, stride):
super(Deconvolution, self).__init__()
if stride == 2:
kernel_size = 3
output_padding = 1
elif stride == 4:
kernel_size = 5
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.model_zoo
assert_size_stride = torch._C... | guoyongcs/HNAS | Deconvolution | false | 15,473 | [
"MIT"
] | 60 | 2b34e1b637bb03d23ca6559c1b5d1245d9744348 | https://github.com/guoyongcs/HNAS/tree/2b34e1b637bb03d23ca6559c1b5d1245d9744348 |
CNNCifar | # 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... | FANJIYU0825/federated-learning | CNNCifar | false | 1,874 | [
"MIT"
] | 0 | 5772ca0a321a222eae5d5e29b70fb4a468c28374 | https://github.com/FANJIYU0825/federated-learning/tree/5772ca0a321a222eae5d5e29b70fb4a468c28374 |
FeatureCorrelation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | Karenou/mmfashion | FeatureCorrelation | false | 9,459 | [
"Apache-2.0"
] | 0 | dfc334232d1700cde18d144f983dd5b0a7f9852a | https://github.com/Karenou/mmfashion/tree/dfc334232d1700cde18d144f983dd5b0a7f9852a |
BinaryReg | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | devaansh100/pytorch_connectomics | BinaryReg | false | 6,555 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
scale_feature | import torch
import torch.nn as nn
class scale_feature(nn.Module):
def __init__(self, scale):
super(scale_feature, self).__init__()
self.scale = scale
def forward(self, x):
return self.scale * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
re... | 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... | tommy90191/Find_Tiny_but_Important_Image_Changes | scale_feature | false | 4,444 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
GraphConvolution | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
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.nn import Module
i... | JinmiaoChenLab/SEDR | GraphConvolution | false | 17,491 | [
"MIT"
] | 5 | 18616dfe2ecb56e22225ffefe949d353e819a7d8 | https://github.com/JinmiaoChenLab/SEDR/tree/18616dfe2ecb56e22225ffefe949d353e819a7d8 |
MaxPoolingAggregator_1 | import torch
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
... | lavis-nlp/irtm | MaxPoolingAggregator_1 | false | 10,409 | [
"MIT"
] | 0 | e6c96519918795cfaa0c09ef2d4164f451265518 | https://github.com/lavis-nlp/irtm/tree/e6c96519918795cfaa0c09ef2d4164f451265518 |
KernelTensorRingWithCategoryAndState | import math
import torch
from torch import nn
from torch.nn import Parameter
class KernelTensorRingWithCategoryAndState(nn.Module):
def __init__(self, amount_of_categories, first_rank, m, second_rank):
super(KernelTensorRingWithCategoryAndState, self).__init__()
self.first_rank = first_rank
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn import Parameter
assert_size_stri... | AndresOtero/TensorDecompositionMachineLearning | KernelTensorRingWithCategoryAndState | false | 16,913 | [
"MIT"
] | 3 | 455f16b405ec9d031999b0ebf9c5a68d3c20b233 | https://github.com/AndresOtero/TensorDecompositionMachineLearning/tree/455f16b405ec9d031999b0ebf9c5a68d3c20b233 |
CriticNet | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
class CriticNet(nn.Module):
def __init__(self, args):
super(CriticNet, self).__init__()
state_dim = args.state_dim
action_dim =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Manojbhat09/Sane-annotation-shape-complete | CriticNet | false | 18,389 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ZurMaD/DAIN | BasicBlock | false | 3,013 | [
"MIT"
] | 0 | 22570e51e84f7dfd48ba4f88e6ee7c9ff1b0b123 | https://github.com/ZurMaD/DAIN/tree/22570e51e84f7dfd48ba4f88e6ee7c9ff1b0b123 |
ResBlock2 | # 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.... | cvmlarun/RANet | ResBlock2 | false | 6,514 | [
"Apache-2.0"
] | 1 | 3f67a3f36aaacd9cc7fb98ec79f77db8f1ebdc60 | https://github.com/cvmlarun/RANet/tree/3f67a3f36aaacd9cc7fb98ec79f77db8f1ebdc60 |
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.... | NathanYanJing/TransformerReplication | EncoderLayer | false | 11,749 | [
"MIT"
] | 0 | b20f987dcc507724971f843c2d214c9c76bd8e34 | https://github.com/NathanYanJing/TransformerReplication/tree/b20f987dcc507724971f843c2d214c9c76bd8e34 |
Encoder2 | import torch
import torch.nn as nn
class Encoder2(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder2, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0, dilation=1)
self.conv12 = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MingSun-Tse/Collaborative-Distillation | Encoder2 | false | 14,040 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
adaILN | # 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 libdevice, math as tl_math
import torc... | Elvinky/IEGAN | adaILN | false | 8,057 | [
"MIT"
] | 29 | db072e38fb022b367da24d3210c59136fbad224e | https://github.com/Elvinky/IEGAN/tree/db072e38fb022b367da24d3210c59136fbad224e |
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.... | Holmes-Alan/TxST | ResidualBlock | false | 9,250 | [
"MIT"
] | 0 | c5b59a12bbb9e62244c3b608581d5cb9606525e0 | https://github.com/Holmes-Alan/TxST/tree/c5b59a12bbb9e62244c3b608581d5cb9606525e0 |
DistillKL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Electronicshelf/Few-shot-regularization | DistillKL | false | 404 | [
"MIT"
] | 0 | 3fd0fef52684af77a5e574b5d61cfd8dd557b14b | https://github.com/Electronicshelf/Few-shot-regularization/tree/3fd0fef52684af77a5e574b5d61cfd8dd557b14b |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Rming/Self-Correction-Human-Parsing | GlobalAvgPool2d | false | 977 | [
"MIT"
] | 0 | c2b711c0a11f3980a8bf4c7a2acf85d80732620a | https://github.com/Rming/Self-Correction-Human-Parsing/tree/c2b711c0a11f3980a8bf4c7a2acf85d80732620a |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
... | 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.optim
assert_size_stride = torch._C._dynamo.... | Arvindkrishna1997/comet-dataset | LayerNorm | false | 4,874 | [
"Apache-2.0"
] | 1 | 2cb42a4aefdea6d0e81f544f94830d44730e9853 | https://github.com/Arvindkrishna1997/comet-dataset/tree/2cb42a4aefdea6d0e81f544f94830d44730e9853 |
BCELoss2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ekalyashov/segmentation-unet | BCELoss2d | false | 12,337 | [
"MIT"
] | 0 | 59dc95419481b2535a52332e0be92b15c7450674 | https://github.com/ekalyashov/segmentation-unet/tree/59dc95419481b2535a52332e0be92b15c7450674 |
ILN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class ILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(ILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = Parameter(torch.Tensor(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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | SubZero12556/Cats2dogs_ONNX | ILN | false | 14,443 | [
"MIT"
] | 2,519 | 52a6a60d519e23b02f0847f0fa9f9ead89ca5f4e | https://github.com/SubZero12556/Cats2dogs_ONNX/tree/52a6a60d519e23b02f0847f0fa9f9ead89ca5f4e |
conv_head_pooling | import torch
from torch import nn
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(conv_head_pooling, self).__init__()
self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=stride +
1, padding=stride // 2, stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | naver-ai/PfLayer | conv_head_pooling | false | 16,133 | [
"Apache-2.0"
] | 59 | da8f80b2ea3b6bd7fbee3beee8b1516c89bc0441 | https://github.com/naver-ai/PfLayer/tree/da8f80b2ea3b6bd7fbee3beee8b1516c89bc0441 |
ResidualConvUnit | import torch
import torch.nn as nn
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ShiraLightricks/3d-photo-inpainting | ResidualConvUnit | false | 1,077 | [
"MIT"
] | 0 | c42ac41576690b765e50f5281ddbfb58439ff36d | https://github.com/ShiraLightricks/3d-photo-inpainting/tree/c42ac41576690b765e50f5281ddbfb58439ff36d |
PosLinear2 | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear2(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
return nn.functional.linear(x, torch.nn.functional.softmax(self.
weight, 1), self.bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JunLi-Galios/CP-Flow | PosLinear2 | false | 11,592 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss 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 libdevice, math as tl_math
import torc... | youqingxiaozhua/ABAW3 | FocalLoss | false | 11,062 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
FastGRNNCell | # 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 ... | Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML | FastGRNNCell | false | 14,416 | [
"MIT"
] | 719 | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | https://github.com/Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML/tree/ef9f8a77f096acbdeb941014791f8eda1c1bc35b |
AttentionBlock | # 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.... | JINHXu/NeMo | AttentionBlock | false | 11,626 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
SE | # 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 itertools import chain a... | JaywongWang/SlowFast | SE | false | 8,371 | [
"Apache-2.0"
] | 43 | 366467aafc856712fdc3e9c4cce8e90969047ee6 | https://github.com/JaywongWang/SlowFast/tree/366467aafc856712fdc3e9c4cce8e90969047ee6 |
FusionMax | import torch
import torch.nn as nn
class Fusion(nn.Module):
""" Base Fusion Class"""
def __init__(self, input_dim=3):
super().__init__()
self.input_dim = input_dim
def tile_x2(self, x1, x2, x2_proj=None):
if x2_proj:
x2 = x2_proj(x2)
x2 = x2.unsqueeze(-1).unsq... | 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... | wx-b/cliport | FusionMax | false | 16,733 | [
"Apache-2.0"
] | 110 | c29b0c4b6b1c4e4da5bda6c7f8c718e36f28a6e8 | https://github.com/wx-b/cliport/tree/c29b0c4b6b1c4e4da5bda6c7f8c718e36f28a6e8 |
Recall | # 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... | ChristophReich1996/Cell-DETR | Recall | false | 13,495 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
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
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | dugusword/transformer | MultiHeadAttention | false | 6,612 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
LinearScale | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | xh-liu-tech/CIPS-3D | LinearScale | false | 11,102 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
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.... | gnzeleven/Hand-Written-Digits-Recognition-Web-App | CNN | false | 3,560 | [
"Apache-2.0"
] | 0 | b2c654f8b897273323a4930e3064b843b45cd5c6 | https://github.com/gnzeleven/Hand-Written-Digits-Recognition-Web-App/tree/b2c654f8b897273323a4930e3064b843b45cd5c6 |
LxmertAttention | import math
import torch
from torch import nn
from itertools import *
class LxmertAttention(nn.Module):
def __init__(self, hidden_size, num_attention_heads,
attention_probs_dropout_prob, ctx_dim):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ashutoshbsathe/SmBop | LxmertAttention | false | 9,813 | [
"MIT"
] | 0 | ce5f67ec070df55b84d7f3617659011732020c96 | https://github.com/ashutoshbsathe/SmBop/tree/ce5f67ec070df55b84d7f3617659011732020c96 |
PoolingF | # 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.utils.data
impo... | Theomat/colorization-av-enseirb-2020 | PoolingF | false | 14,470 | [
"Apache-2.0"
] | 1,422 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | https://github.com/Theomat/colorization-av-enseirb-2020/tree/c54c2388ea39a62289fa2f1c51b4757bf55d3c4f |
EncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Rajathbharadwaj/algorithmic-efficiency | EncoderLayer | false | 14,331 | [
"Apache-2.0"
] | 49 | 47d2928836e0574bc54cc3ad58860dd4daf86cce | https://github.com/Rajathbharadwaj/algorithmic-efficiency/tree/47d2928836e0574bc54cc3ad58860dd4daf86cce |
LocalResponseNormLayer | # 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_... | ndey96/lucent | LocalResponseNormLayer | false | 10,618 | [
"Apache-2.0"
] | 0 | d868d8ca52520bd245c1e5fcf3b026782f77e561 | https://github.com/ndey96/lucent/tree/d868d8ca52520bd245c1e5fcf3b026782f77e561 |
Standardscaler | # 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
assert_size_stride = torch._... | Stuksus/StandardScaler_for_pytorch | Standardscaler | false | 1,088 | [
"MIT"
] | 0 | 27da9afd111007f20a615bee9a5a7ac272adb241 | https://github.com/Stuksus/StandardScaler_for_pytorch/tree/27da9afd111007f20a615bee9a5a7ac272adb241 |
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.... | aquibjaved/ABSA-PyTorch | Attention | false | 9,768 | [
"MIT"
] | 0 | fd904250ceec436e49dc50694f79891c0c67d6b1 | https://github.com/aquibjaved/ABSA-PyTorch/tree/fd904250ceec436e49dc50694f79891c0c67d6b1 |
ELU | # 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... | altescy/xtorch | ELU | false | 9,709 | [
"MIT"
] | 0 | bcbbbe645f4d62c211af5b3555c526cc60792c32 | https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32 |
AGRUCell | # 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 ... | Ahren09/RecBole | AGRUCell | false | 1,929 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
Projection | import torch
from typing import Tuple
class Projection(torch.nn.Module):
"""
| A class for a projection of an input to a different shape effectively mapping from
| [..., inshape[1] .. inshape[-1]] -> [..., outshape[1] .. outshape[-1]]
| only going over the subelements.
| Example input (4,6) to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Tuple
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty... | kpoeppel/pytorch_probgraph | Projection | false | 15,851 | [
"BSD-3-Clause"
] | 47 | b78595ab03bbe92595ad2f6b35f5dd8bf84d6da0 | https://github.com/kpoeppel/pytorch_probgraph/tree/b78595ab03bbe92595ad2f6b35f5dd8bf84d6da0 |
Img_decoder_v3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, input_channel, output_channel, upsample=True):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channel, output_channel, kernel_size=3,
padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Holmes-Alan/Photo2Sketch | Img_decoder_v3 | false | 638 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
NN | # 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_... | AsianZeus/PyTorch-Models | NN | false | 8,842 | [
"Apache-2.0"
] | 0 | 3249a06a5233b22232a8a336c52e8c24d1b55439 | https://github.com/AsianZeus/PyTorch-Models/tree/3249a06a5233b22232a8a336c52e8c24d1b55439 |
Attention | import torch
from torch import Tensor
from torch import nn
class Attention(nn.Module):
def forward(self, selected_input: 'Tensor', attention: 'Tensor'):
attended_input = selected_input * attention.unsqueeze(-1)
return attended_input
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | zsl24/voice-activity-detection | Attention | false | 16,827 | [
"MIT"
] | 74 | a034be23c6283121c6b72e778c6ff6711045cbe3 | https://github.com/zsl24/voice-activity-detection/tree/a034be23c6283121c6b72e778c6ff6711045cbe3 |
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 libdevice, math as tl_math
import torc... | Mo5mami/retinanet-examples | FocalLoss | false | 14,053 | [
"BSD-3-Clause"
] | 848 | f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 | https://github.com/Mo5mami/retinanet-examples/tree/f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 |
VariableBoxMLP | import torch
import torch.optim
import torch.jit
import torch.nn as nn
class VariableBoxMLP(nn.Module):
def __init__(self, num_in_features: 'int', num_out_features: 'int',
neurons_per_layer: 'int', hidden_layers: 'int'):
super(VariableBoxMLP, self).__init__()
self.hidden_layers = hidden_l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.optim
... | plaveczlambert/nonlinearbubbledynamics | VariableBoxMLP | false | 10,671 | [
"MIT"
] | 0 | 190c5170f7ff6068badeee818c01226c55aaec97 | https://github.com/plaveczlambert/nonlinearbubbledynamics/tree/190c5170f7ff6068badeee818c01226c55aaec97 |
L1DepthLoss | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class L1DepthLoss(nn.Module):
"""Custom L1 loss for depth sequences."""
def __init__(self, args):
super(L1DepthLoss, self).__init__()
self.args = args
self.word_dim = 1
def forward(self, predictions,... | 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... | AnReu/structural-probes | L1DepthLoss | false | 1,880 | [
"Apache-2.0"
] | 0 | fdc99dc124fa6df3dbdd5ba48a90f08bb6bf37b7 | https://github.com/AnReu/structural-probes/tree/fdc99dc124fa6df3dbdd5ba48a90f08bb6bf37b7 |
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