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 |
|---|---|---|---|---|---|---|---|---|---|---|
CNN_decoder_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.... | bwalker1/graph-generation | CNN_decoder_attention | false | 10,056 | [
"MIT"
] | 0 | e068769cb021760eb2549ced382b1a217609db86 | https://github.com/bwalker1/graph-generation/tree/e068769cb021760eb2549ced382b1a217609db86 |
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.... | AnetaKaczynska/ProtoPNet | LossAttentionLayer | false | 1,952 | [
"MIT"
] | 0 | 7de2aa57833586ccfd8e63dc835c8cc9db727a2f | https://github.com/AnetaKaczynska/ProtoPNet/tree/7de2aa57833586ccfd8e63dc835c8cc9db727a2f |
ConvBlock | import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, norm=None):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(input_size, output_size, kernel_size, stride,
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | MatusBako/MakeFacesGreatAgain | ConvBlock | false | 828 | [
"MIT"
] | 0 | e4941a8460db79dec566ed02d4b23eafb416a6db | https://github.com/MatusBako/MakeFacesGreatAgain/tree/e4941a8460db79dec566ed02d4b23eafb416a6db |
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.... | jamesYu365/Transfomer-example | EncoderLayer | false | 12,720 | [
"MIT"
] | 0 | a867f72f539de9746668da411f524dab45ddf12f | https://github.com/jamesYu365/Transfomer-example/tree/a867f72f539de9746668da411f524dab45ddf12f |
DDM_Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | MLforHealth/state_representations_for_RLinHealth | DDM_Encoder | false | 8,523 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
SuperPointNet | # 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.... | MamonaAwan/UnsupervisedLandmarks | SuperPointNet | false | 8,544 | [
"MIT"
] | 26 | 89180755b891fd28e0199560d628dc8b0d2b3e68 | https://github.com/MamonaAwan/UnsupervisedLandmarks/tree/89180755b891fd28e0199560d628dc8b0d2b3e68 |
ShuffleCat | import torch
import torch.nn as nn
class ShuffleCat(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
a = a.permute(0, 2, 3, 1).contiguous().view(-1, c)
b = b.permute(0, 2, 3, 1).contiguous().view(-1, c)
x = torch.cat((a, b), dim=0).tra... | 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... | AbhinandanVellanki/yolact_edge | ShuffleCat | false | 1,954 | [
"MIT"
] | 0 | 06d6318cf70ef511b19aa1c14f0476e4ffac2722 | https://github.com/AbhinandanVellanki/yolact_edge/tree/06d6318cf70ef511b19aa1c14f0476e4ffac2722 |
ScaleNorm | # 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
from torch import nn
assert_... | ncoop57/x-transformers | ScaleNorm | false | 10,609 | [
"MIT"
] | 0 | b65f25384349abfc101001b42482b05745c861fa | https://github.com/ncoop57/x-transformers/tree/b65f25384349abfc101001b42482b05745c861fa |
BCELoss | # 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 ... | RichardScottOZ/sota-data-augmentation-and-optimizers | BCELoss | false | 8,749 | [
"MIT"
] | 31 | 60128ca762ac2864a3b54c43c36d1d5aa2033e5a | https://github.com/RichardScottOZ/sota-data-augmentation-and-optimizers/tree/60128ca762ac2864a3b54c43c36d1d5aa2033e5a |
logreg | # 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 ... | cjbumgardner/HE_for_Medical_Data | logreg | false | 6,452 | [
"MIT"
] | 1 | 248dcd8b48924fe1f6edbeee4e16282d4a31069a | https://github.com/cjbumgardner/HE_for_Medical_Data/tree/248dcd8b48924fe1f6edbeee4e16282d4a31069a |
DPFP | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class DPFP(Module):
"""
## Deterministic Parameter Free Project (DPFP)
This is the new projection function $\\color{lightgreen}{\\phi}$ introduced in the paper.
DPFP ... | 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.... | ppvalluri09/annotated_deep_learning_paper_implementations | DPFP | false | 11,066 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
IOU_Loss | import torch
import torch.nn as nn
class IOU_Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_pred, y):
i = y_pred.mul(y)
u = y_pred + y - i
mean_iou = torch.mean(i.view(i.shape[0], -1).sum(1) / u.view(i.
shape[0], -1).sum(1))
io... | 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... | allen-q/pytorch | IOU_Loss | false | 3,081 | [
"MIT"
] | 0 | 76947f8d6f0bcee04425ad69f93b9a5e3a5060ae | https://github.com/allen-q/pytorch/tree/76947f8d6f0bcee04425ad69f93b9a5e3a5060ae |
Model | # 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 ... | UoA-CARES/BuilT-NLP | Model | false | 2,925 | [
"MIT"
] | 0 | 761798cbce51d91ec24171e9159413e51c0e0e62 | https://github.com/UoA-CARES/BuilT-NLP/tree/761798cbce51d91ec24171e9159413e51c0e0e62 |
Block | # 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 ... | ZhijieXiao-0624/CNXA | Block | false | 3,005 | [
"MIT"
] | 0 | a63b3561010cf87f696a005f8ea252e7cdaa7ca2 | https://github.com/ZhijieXiao-0624/CNXA/tree/a63b3561010cf87f696a005f8ea252e7cdaa7ca2 |
SEModule | import torch
import torch.nn as nn
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool3d(1)
self.bottleneck = self._round_width(channels, reduction)
self.fc1 = nn.Conv3d(channels, self.bottleneck, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alexis-Fab/mmaction2 | SEModule | false | 11,217 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
Swish | # 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... | MLIA/LEADS | Swish | false | 17,642 | [
"MIT"
] | 6 | 4010f6b6e6a56ee049b4b4a9aec1c24b34730616 | https://github.com/MLIA/LEADS/tree/4010f6b6e6a56ee049b4b4a9aec1c24b34730616 |
LayerNorm | import torch
import torch.nn as nn
import torch.utils.data
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.Parameter(torch.zeros(features))
self.eps = eps
def... | 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.utils.data
assert_size_stride = torch._C._dy... | FadedCosine/Dependency-Guided-Neural-Text-Generation | LayerNorm | false | 8,090 | [
"Apache-2.0"
] | 19 | 600ad563ce240c7807f839f7eee5251616b9325b | https://github.com/FadedCosine/Dependency-Guided-Neural-Text-Generation/tree/600ad563ce240c7807f839f7eee5251616b9325b |
HeatmapLoss | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing
class HeatmapLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt, mask):
assert pred.size() =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.m... | ducongju/Scale-sensitive-Heatmap | HeatmapLoss | false | 1,867 | [
"MIT"
] | 0 | 4016610ba96a6a6645895bbf4bcdb3ff4690a2d8 | https://github.com/ducongju/Scale-sensitive-Heatmap/tree/4016610ba96a6a6645895bbf4bcdb3ff4690a2d8 |
WeightedBCELoss2d | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class WeightedBCELoss2d(nn.Module):
def __init__(self):
super(WeightedBCELoss2d, self).__init__()
def forward(self, logits, labels, weights):
w = weights.view(-1)
logits = logits.view(-1)
g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | jayden-chua/image-mask | WeightedBCELoss2d | false | 3,699 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
GeLU | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.... | abhipsabasu/rubi.bootstrap.pytorch | GeLU | false | 14,731 | [
"BSD-3-Clause"
] | 83 | 9fa9639c1ee4a040958d976eeb5dca2dd2203980 | https://github.com/abhipsabasu/rubi.bootstrap.pytorch/tree/9fa9639c1ee4a040958d976eeb5dca2dd2203980 |
SqueezeExcitation | import torch
import torch.nn as nn
import torch.nn.functional as F
def make_divisible(v, divisor=8, min_value=None):
"""
The channel number of each layer should be divisable by 8.
The function is taken from
github.com/rwightman/pytorch-image-models/master/timm/models/layers/helpers.py
"""
min_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | akashAD98/EfficientNetv2-with-Detectron2 | SqueezeExcitation | false | 3,055 | [
"Apache-2.0"
] | 0 | 1ba7f32cda31550ed4a040c15271612fa3f73d74 | https://github.com/akashAD98/EfficientNetv2-with-Detectron2/tree/1ba7f32cda31550ed4a040c15271612fa3f73d74 |
AE | # 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_... | PtrMan/21V1 | AE | false | 5,726 | [
"MIT"
] | 1 | fbac4deb5bec3a5e50b81e1e91c4a8a9820d6aaa | https://github.com/PtrMan/21V1/tree/fbac4deb5bec3a5e50b81e1e91c4a8a9820d6aaa |
TransposeMultiheadAttention | import torch
import torch.nn as nn
import torch.utils.data
from typing import Optional
import torch.nn
class TransposeMultiheadAttention(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_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
from torch._inductor.runtime.... | denred0/pytorchvideo | TransposeMultiheadAttention | false | 1,872 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 |
DetachModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class DetachModel(torch.nn.Module):
def __init__(self):
super(DetachModel, self).__init__()
def forward(self, a):
b = a.detach()
return b + b
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_input... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | DetachModel | false | 12,551 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
DQN_hot4 | # 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 ... | CoAxLab/azad | DQN_hot4 | false | 17,173 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
EqualConv2d | # 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... | BillyXYB/TransEditor | EqualConv2d | false | 17,061 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
kl_loss | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
class kl_loss(Module):
def __init__(self, num_nodes, num_edges):
super(kl_loss, self).__init__()
self.num_nodes = num_nodes
self.num_edges = num_edges
def forward(self, z_node_log_std, z_node_mean, z_... | 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.nn import Module
from torch.nn.modules.module import Module
as... | iMoonLab/HHDTI | kl_loss | false | 6,843 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
GeometryFeature | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class GeometryFeature(nn.Module):
def __init__(self):
super(GeometryFeature, self).__init__()
def forward(self, z, vnorm, unorm, h, w, ch, cw, fh, fw):
x = z * (0.5 * h * (vnorm + 1) - ch) ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | phatli/PENet_ICRA2021 | GeometryFeature | false | 4,124 | [
"MIT"
] | 0 | 18594b8f11d4d99022d9c80a86a6e2d4e854404a | https://github.com/phatli/PENet_ICRA2021/tree/18594b8f11d4d99022d9c80a86a6e2d4e854404a |
Conv2dZeros | # 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.... | AbnerVictor/HCFlow | Conv2dZeros | false | 9,104 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b |
SEBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = nn.Conv2d(in_channels=input_channels, out_channels=
internal_neurons, kernel_size=1, stride=1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | arsalan0004/6DRepNet | SEBlock | false | 14,904 | [
"MIT"
] | 84 | cdfb2b151785eb89fef70907a6f2a19fa0acf4ae | https://github.com/arsalan0004/6DRepNet/tree/cdfb2b151785eb89fef70907a6f2a19fa0acf4ae |
RNNCell | import torch
import torch.nn as nn
class RNNCell(nn.Module):
def __init__(self, embed_dim, hidden_size, vocab_dim):
super().__init__()
self.hidden_size = hidden_size
self.input2hidden = nn.Linear(embed_dim + hidden_size, hidden_size)
def forward(self, inputs, hidden):
combine... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | saidulislam/RNN-Sequential-Data-Processing | RNNCell | false | 12,983 | [
"Apache-2.0"
] | 0 | 2e043f37f9a67177a3dc19cbfe67d187c9cbb5f9 | https://github.com/saidulislam/RNN-Sequential-Data-Processing/tree/2e043f37f9a67177a3dc19cbfe67d187c9cbb5f9 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W ... | 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... | CharlesPikachu/CharlesFace | Upsample | false | 7,842 | [
"MIT"
] | 13 | 90bfe38c58068228d0069dce43b55b2570acaa16 | https://github.com/CharlesPikachu/CharlesFace/tree/90bfe38c58068228d0069dce43b55b2570acaa16 |
ConvElu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | prstrive/EPCDepth | ConvElu | false | 16,284 | [
"MIT"
] | 76 | 84119c806741334b652749ee953e3eab60a3718c | https://github.com/prstrive/EPCDepth/tree/84119c806741334b652749ee953e3eab60a3718c |
Custom_dropout | # 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.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Chahalprincy/deepchem | Custom_dropout | false | 224 | [
"MIT"
] | 0 | 9d1a6a879cc74b065694b3ddb763d52151d57b7a | https://github.com/Chahalprincy/deepchem/tree/9d1a6a879cc74b065694b3ddb763d52151d57b7a |
QuadriLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class QuadriLinearScore(nn.Module):
"""
Outer product version of quadrilinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data.dataloader
import torc... | db-bionlp/CLNER | QuadriLinearScore | false | 15,165 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
LinearScale | import torch
from torch import nn
class LinearScale(nn.Module):
def __init__(self, scale, bias):
super(LinearScale, self).__init__()
self.scale_v = scale
self.bias_v = bias
pass
def forward(self, x):
out = x * self.scale_v + self.bias_v
return out
def __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 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 |
GINPreTransition | # 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 typing
impor... | FaezehAmou2020/torch_gnn | GINPreTransition | false | 9,049 | [
"BSD-3-Clause"
] | 0 | 996a7f94259e718c625c6b4594729f025c4e4f14 | https://github.com/FaezehAmou2020/torch_gnn/tree/996a7f94259e718c625c6b4594729f025c4e4f14 |
ConsinSimilarityLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Geson-anko/VQ_AutoEncoder | ConsinSimilarityLoss | false | 2,274 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
DDPGActorCont | # 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.... | iffiX/machin | DDPGActorCont | false | 15,600 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
Rectifier | import torch
import torch.nn as nn
import torch.optim
class Rectifier(nn.Module):
def __init__(self, l=-0.1, r=1.1):
super().__init__()
self.l = l
self.r = r
self.eps = 1e-07
def forward(self, x, l=None, r=None):
l = l if l is not None else self.l
r = r if r i... | 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.optim
assert_size_stride = torch._C._dynamo.guards.ass... | ovechkinVT/SkipRNN | Rectifier | false | 7,433 | [
"MIT"
] | 1 | 7c1f37349d464b1b6bf8835520abad22b199f1ad | https://github.com/ovechkinVT/SkipRNN/tree/7c1f37349d464b1b6bf8835520abad22b199f1ad |
MaxPool2dSamePadding | # 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_siz... | Jintao-Huang/EfficientDet_PyTorch | MaxPool2dSamePadding | false | 8,365 | [
"Apache-2.0"
] | 18 | 79616be397b7f57992cd43b772f65b58b5e25a8b | https://github.com/Jintao-Huang/EfficientDet_PyTorch/tree/79616be397b7f57992cd43b772f65b58b5e25a8b |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Anqi-nus/trtpose | CmapPafHeadAttention | false | 4,912 | [
"MIT"
] | 1 | 723ec95df8b8414b9289af90fbfbc98756792a21 | https://github.com/Anqi-nus/trtpose/tree/723ec95df8b8414b9289af90fbfbc98756792a21 |
SigmaL1SmoothLoss | # 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
... | LaurenSpiegel/fastai_docs | SigmaL1SmoothLoss | false | 753 | [
"Apache-2.0"
] | 0 | 4fe6b62116d88dea9610548133e6cadb6b260a73 | https://github.com/LaurenSpiegel/fastai_docs/tree/4fe6b62116d88dea9610548133e6cadb6b260a73 |
Reconstruction_Layer | import torch
class Reconstruction_Layer(torch.nn.Module):
"""TThis is the reconstruction layer for the network to learn how to remake
the original input image"""
def __init__(self, batch_size, capsin_n_maps, capsin_n_dims,
reconstruct_channels):
super(Reconstruction_Layer, self).__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
assert_size_stride = torch._C... | arjunsbalaji/oct | Reconstruction_Layer | false | 1,478 | [
"Apache-2.0"
] | 0 | f21e11f6dda952cd914444512ddadb4141757951 | https://github.com/arjunsbalaji/oct/tree/f21e11f6dda952cd914444512ddadb4141757951 |
Conv2d | # 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_... | Elin24/DCL-CrowdCounting | Conv2d | false | 8,043 | [
"MIT"
] | 12 | 2f8e68a2d29a8599e795b502f21b4de778e6214c | https://github.com/Elin24/DCL-CrowdCounting/tree/2f8e68a2d29a8599e795b502f21b4de778e6214c |
GCNModelVAE | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | hurraygong/scGNN | GCNModelVAE | false | 6,831 | [
"MIT"
] | 1 | bc555895fbd5740ddd82e03187171116889cc10e | https://github.com/hurraygong/scGNN/tree/bc555895fbd5740ddd82e03187171116889cc10e |
MultiHeadAttention | import torch
import torchvision.transforms.functional as F
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 = temperatur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yshen47/mvsnerf | MultiHeadAttention | false | 11,033 | [
"MIT"
] | 0 | 38ab4cf4fc5d025a9ad04e4a801b501ea9a78fb4 | https://github.com/yshen47/mvsnerf/tree/38ab4cf4fc5d025a9ad04e4a801b501ea9a78fb4 |
RMSNorm | # 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
from torch import nn
assert_... | booydar/x-transformers | RMSNorm | false | 3,234 | [
"MIT"
] | 0 | 97f0a854fdf4df8a3fbf6a580e2375463af3538c | https://github.com/booydar/x-transformers/tree/97f0a854fdf4df8a3fbf6a580e2375463af3538c |
Encoder | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | slgao/FU-DeepLearningCourse | Encoder | false | 4,364 | [
"MIT"
] | 0 | 2300e8bdaa2afb4c73535d5de80874f6103af6f2 | https://github.com/slgao/FU-DeepLearningCourse/tree/2300e8bdaa2afb4c73535d5de80874f6103af6f2 |
LayerNorm | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.normalization import LayerNorm
from torch.optim.lr_scheduler import *
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=0.0001):
super(LayerNorm, self).__init__()... | 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 import Parameter
from torch.nn.parameter im... | chunhuililili/mt_dnn | LayerNorm | false | 10,192 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
CrossEntropyLossLabelSmoothing | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
def _is_long(x):
if hasattr(x, 'data'):
x = x.data
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representat... | 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... | litvinich/detectron2 | CrossEntropyLossLabelSmoothing | false | 12,724 | [
"Apache-2.0"
] | 0 | ac622e22eb0f13c9b5838a1e45b046212f22f814 | https://github.com/litvinich/detectron2/tree/ac622e22eb0f13c9b5838a1e45b046212f22f814 |
SigmoidDeepLiftModel | # 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_... | ngduduong/captum | SigmoidDeepLiftModel | false | 4,079 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
Conv_ReLU_Block | import torch
import torch.nn as nn
class Conv_ReLU_Block(nn.Module):
def __init__(self):
super(Conv_ReLU_Block, self).__init__()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=
3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | advaza/pytorch-vdsr | Conv_ReLU_Block | false | 12,065 | [
"MIT"
] | 0 | 8011f7323de3c7756df3828612addfb122c2bfef | https://github.com/advaza/pytorch-vdsr/tree/8011f7323de3c7756df3828612addfb122c2bfef |
LandmarkHead | import torch
import torch.nn as nn
from itertools import product as product
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 itertools import product as product
assert_size_strid... | ai18435136351/facenet-retinaface-pytorch | LandmarkHead | false | 14,784 | [
"MIT"
] | 48 | f228969e46d7402170b708798a210de552879d16 | https://github.com/ai18435136351/facenet-retinaface-pytorch/tree/f228969e46d7402170b708798a210de552879d16 |
DomainClassifier | import torch
import torch.nn.parallel
import torch.optim
import torch.nn as nn
class DomainClassifier(nn.Module):
def __init__(self, input_dim=1024, ndf=64, with_bias=False):
super(DomainClassifier, self).__init__()
self.conv1 = nn.Conv2d(input_dim, ndf, kernel_size=4, stride=2,
paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.optim
import torch.nn as nn
assert_size_st... | chaneyddtt/UDA-Animal-Pose | DomainClassifier | false | 15,096 | [
"MIT"
] | 61 | f1ebfda860a2585c60fe86ce1632e910ac97ebc5 | https://github.com/chaneyddtt/UDA-Animal-Pose/tree/f1ebfda860a2585c60fe86ce1632e910ac97ebc5 |
MLPBase | import torch
from torch import nn
import torch.nn.functional as F
class MLPBase(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(MLPBase, self).__init__()
self.l1 = nn.Linear(num_inputs, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, num_outputs)
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 import nn
assert_s... | TachikakaMin/dreamer-torch | MLPBase | false | 1,122 | [
"MIT"
] | 0 | 3c99526f4507e28cf8b34ada0321001adcf8ae1f | https://github.com/TachikakaMin/dreamer-torch/tree/3c99526f4507e28cf8b34ada0321001adcf8ae1f |
ODEfunc | # 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.... | TylerChoi1224/torchdiffeq | ODEfunc | false | 1,191 | [
"MIT"
] | 0 | 72f74d9651a58ab11cdadd60682f1b61e625ef53 | https://github.com/TylerChoi1224/torchdiffeq/tree/72f74d9651a58ab11cdadd60682f1b61e625ef53 |
F_fully_convolutional | import torch
import torch.nn as nn
import torch.nn.functional as F
class F_fully_convolutional(nn.Module):
def __init__(self, in_channels, out_channels, internal_size=256,
kernel_size=3, leaky_slope=0.02):
super().__init__()
pad = kernel_size // 2
self.leaky_slope = leaky_slope
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 |
TransNonlinear | # 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.... | Jasonkks/PTTR | TransNonlinear | false | 8,345 | [
"Apache-2.0"
] | 14 | 11f664a7f1b2281293d82a5450fdd3d4bfa5883e | https://github.com/Jasonkks/PTTR/tree/11f664a7f1b2281293d82a5450fdd3d4bfa5883e |
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.nn as nn
assert_... | NguyenTheAn/AdaptiveWingLoss | BasicBlock | false | 9,363 | [
"Apache-2.0"
] | 0 | abaade9521c1382739a158f3ad5ce493948add1d | https://github.com/NguyenTheAn/AdaptiveWingLoss/tree/abaade9521c1382739a158f3ad5ce493948add1d |
BehlerAngular | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | blindcharzard/AttnSchNet | BehlerAngular | false | 12,171 | [
"MIT"
] | 0 | 297bd130086459be6b732d68377193e244536bfc | https://github.com/blindcharzard/AttnSchNet/tree/297bd130086459be6b732d68377193e244536bfc |
L2 | import torch
import torch.nn as nn
from torchvision.transforms import *
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride =... | Haabibi/RBPN-PyTorch | L2 | false | 5,259 | [
"MIT"
] | 1 | 0b04420b384fcc8f78a7b9afeca179fa6c0332c2 | https://github.com/Haabibi/RBPN-PyTorch/tree/0b04420b384fcc8f78a7b9afeca179fa6c0332c2 |
RAddInt | import torch
class RAddInt(torch.nn.Module):
def __init__(self):
super(RAddInt, self).__init__()
def forward(self, x):
return 1 + x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | RAddInt | false | 14,208 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
DiceScore | # 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | jayden-chua/image-mask | DiceScore | false | 3,691 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
NoisyOneHiddenLayer | import torch
import torch as tr
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class quadexp(nn.Module):
def __init__(self, sigma=2.0):
super(quadexp, self).__init__()
self.sigma = sigma
def forward(self, x):
return tr.exp(-x ** 2 / self.sig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MichaelArbel/MMD-gradient-flow | NoisyOneHiddenLayer | false | 17,711 | [
"BSD-3-Clause"
] | 5 | aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 | https://github.com/MichaelArbel/MMD-gradient-flow/tree/aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
""" Loss function based on Dice-Sorensen Coefficient (L = 1 - Dice)
Input arguments:
soft : boolean, default = True
Select whether to use soft labelling or not. If true, dice calculated
directly on sigmoid output without conv... | 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... | Jiongqi/RectAngle | DiceLoss | false | 9,220 | [
"MIT"
] | 0 | 558fa036d1b21b5ae0a556271ab674cd8ffe88b6 | https://github.com/Jiongqi/RectAngle/tree/558fa036d1b21b5ae0a556271ab674cd8ffe88b6 |
D_V | # 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_... | HCShi/IONet | D_V | false | 18,364 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b |
TransformerEncoderLayer | import torch
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aarora8/icefall | TransformerEncoderLayer | false | 3,020 | [
"Apache-2.0"
] | 0 | 8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | https://github.com/aarora8/icefall/tree/8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a |
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... | VisualJoyce/ChengyuBERT | FocalLoss | false | 18,063 | [
"MIT"
] | 8 | 605db3a4b3241dd4d02baa41a68bf23b5b00b36d | https://github.com/VisualJoyce/ChengyuBERT/tree/605db3a4b3241dd4d02baa41a68bf23b5b00b36d |
BertOutput | from _paritybench_helpers import _mock_config
from torch.nn import Module
import torch
import torch.nn as nn
class BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | codecaution/Hetu | BertOutput | false | 1,731 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 |
PatchMerging3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | HarshSulakhe/pytorch_connectomics | PatchMerging3D | false | 9,862 | [
"MIT"
] | 0 | 73402e654afde69a43a5836cc90a32ef75c75dc2 | https://github.com/HarshSulakhe/pytorch_connectomics/tree/73402e654afde69a43a5836cc90a32ef75c75dc2 |
NIN | import string
import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
def _einsum(a, b, c, x, y):
einsum_str = '{},{}->{}'.format(''.join(a), ''.join(b), ''.join(c))
return torch.einsum(einsum_str, x, y)
def contract_inner(x, y):
"""tensordot(x, y, 1)."""
x_chars =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 string
import numpy as np
import torch.utils.data
import torch
import tor... | ayulockin/Image-Super-Resolution-via-Iterative-Refinement | NIN | false | 14,939 | [
"Apache-2.0"
] | 1,764 | 8a75df33d9ed1a2cc0da22f36f576abfc9482913 | https://github.com/ayulockin/Image-Super-Resolution-via-Iterative-Refinement/tree/8a75df33d9ed1a2cc0da22f36f576abfc9482913 |
WeightedFeatureFusion | import torch
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
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
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torc... | csharpshooter/DeepLearning | WeightedFeatureFusion | false | 1,751 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e |
FeedForward | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | ModelTC/EOD | FeedForward | false | 14,061 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
QAvgPooling2d | # 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.autograd import Function
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asser... | XHX00008888/pytorch-quantization-xhx | QAvgPooling2d | false | 9,619 | [
"Apache-2.0"
] | 0 | 8031511f9b9364be006b37b0b3df6c62f765c40a | https://github.com/XHX00008888/pytorch-quantization-xhx/tree/8031511f9b9364be006b37b0b3df6c62f765c40a |
Conv2dSWR | import torch
import torch.utils.data
import torch.nn as nn
import torch
class Conv2dSWR(nn.Module):
def __init__(self, in_channels, out_channels, kernel_radius=2, bias=True):
super(Conv2dSWR, self).__init__()
kernel_size_h = 2 * kernel_radius - 1
self.padding = kernel_radius - 1
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch
assert_size_stride = ... | FVL2020/MSWSR | Conv2dSWR | false | 8,113 | [
"MIT"
] | 27 | 0844e78ee68fb0465efd5c4a2215ce815980526b | https://github.com/FVL2020/MSWSR/tree/0844e78ee68fb0465efd5c4a2215ce815980526b |
CoAttention | import torch
import torch.nn as nn
from torch.nn import functional as F
class CoAttention(nn.Module):
"""
CoAttention encoder
in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604)
check the Figure 2 in paper
* Args:
embed_dim: the number of input e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hamishivi/claf | CoAttention | false | 3,578 | [
"MIT"
] | 0 | 8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 | https://github.com/hamishivi/claf/tree/8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 |
InputConv | import torch
import torch.nn as nn
import torch.nn.functional as F
def _get_padding(kernel_size, stride, dilation):
padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
return padding
class InputConv(nn.Module):
def __init__(self, inp, outp, k=3, stride=1, dilation=1):
super(InputConv, 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
import torch.nn as nn
assert_... | henningpohl/body-based-ar | InputConv | false | 6,800 | [
"MIT"
] | 1 | dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb | https://github.com/henningpohl/body-based-ar/tree/dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb |
ConvGRUCell | # 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... | ValterFallenius/metnet | ConvGRUCell | false | 9,667 | [
"MIT"
] | 0 | 7cde48a7b5fc0b69a8ce9083f934949362620fd5 | https://github.com/ValterFallenius/metnet/tree/7cde48a7b5fc0b69a8ce9083f934949362620fd5 |
RMSE | import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSE(nn.Module):
def __init__(self):
super(RMSE, self).__init__()
def forward(self, fake, real):
if not fake.shape == real.shape:
_, _, H, W = real.shape
fake = F.upsample(fake, size=(H, W), mode... | 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... | d4l3k/crowds | RMSE | false | 12,238 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
ConvReLUNorm | import torch
import torch.utils.data
import torch.nn.functional as F
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AstraliteHeart/cookietts | ConvReLUNorm | false | 7,743 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
Hidden2Discrete | # 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.... | ljw23/ConvLab-2 | Hidden2Discrete | false | 15,931 | [
"Apache-2.0"
] | 339 | 13d48ea0e441701bd66100689b6c25b561f15525 | https://github.com/ljw23/ConvLab-2/tree/13d48ea0e441701bd66100689b6c25b561f15525 |
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 ... | JJuOn/Few-shot_Class_Incremental_Learning | DistillKL | false | 5,369 | [
"MIT"
] | 1 | a2178051a6fefcd73b60f5e4236116bf828a801c | https://github.com/JJuOn/Few-shot_Class_Incremental_Learning/tree/a2178051a6fefcd73b60f5e4236116bf828a801c |
RawScale | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class RawScale(torch.nn.Module):
"""
Scale raw data to [-1, 1] in a symmetric manner, which meets bipolar/unipolar bitstream requirements.
The remaining data count for 'quantile' quantile o... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RuokaiYin/UnarySim | RawScale | false | 5,800 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
MultiNonLinearClassifier | import torch
from torch import nn
class MultiNonLinearClassifier(nn.Module):
def __init__(self, hidden_size, num_label):
super(MultiNonLinearClassifier, self).__init__()
self.num_label = num_label
self.classifier1 = nn.Linear(hidden_size, int(hidden_size / 2))
self.classifier2 = 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
from torch import nn
assert_s... | qhjqhj00/NLI | MultiNonLinearClassifier | false | 12,910 | [
"Apache-2.0"
] | 0 | a5baaf1903e6a22a7bdd1d68a4aaf1680c57d265 | https://github.com/qhjqhj00/NLI/tree/a5baaf1903e6a22a7bdd1d68a4aaf1680c57d265 |
DomainAdaptationLayer | import torch
import torch.nn as nn
class DomainAdaptationLayer(nn.Module):
"""
This class is for the Domain Adaptation Layer. For now, the layer works only in source domain
arguments (function forward):
image: the input image (type: tensor) (size: batch x 384 x W x H)
return (... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Muntasir13/Face-Spoofing-Detection-using-Depth-Wise-Convolution | DomainAdaptationLayer | false | 2,695 | [
"MIT"
] | 0 | f5b1b5d2ad2f29286afbc14e98075534b572c555 | https://github.com/Muntasir13/Face-Spoofing-Detection-using-Depth-Wise-Convolution/tree/f5b1b5d2ad2f29286afbc14e98075534b572c555 |
SpaceToDepth | import torch
from torch import nn
import torch.nn.parallel
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Randl/TResNet | SpaceToDepth | false | 5,752 | [
"Apache-2.0"
] | 1 | 18514caf61d77c7e000a71dde9d1f86ba792b38d | https://github.com/Randl/TResNet/tree/18514caf61d77c7e000a71dde9d1f86ba792b38d |
ShuffleBlock | # 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... | BoyuGuan/pytorch-cifar | ShuffleBlock | false | 9,039 | [
"MIT"
] | 0 | b96d0e325c614e8351449d63742fea5d085fdd8e | https://github.com/BoyuGuan/pytorch-cifar/tree/b96d0e325c614e8351449d63742fea5d085fdd8e |
CapsuleLoss | import torch
import torch.nn.functional as F
from torch import nn
class CapsuleLoss(nn.Module):
def __init__(self):
super(CapsuleLoss, self).__init__()
self.reconstruction_loss = nn.MSELoss(size_average=False)
def forward(self, images, labels, classes, reconstructions):
left = F.relu... | 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... | leftthomas/CapsNet | CapsuleLoss | false | 15,887 | [
"MIT"
] | 163 | 5de2f45daadbe4377df4ccf8a4d31683d7f397bf | https://github.com/leftthomas/CapsNet/tree/5de2f45daadbe4377df4ccf8a4d31683d7f397bf |
GRUCell | import torch
import numpy as np
import torch.nn.functional as F
import torch.utils.data
import torch.nn as nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | systemshift/PyGrid | GRUCell | false | 13,021 | [
"Apache-2.0"
] | 0 | d0ee3df8731a7576d6689fa8b4f5d3fe05ac11ff | https://github.com/systemshift/PyGrid/tree/d0ee3df8731a7576d6689fa8b4f5d3fe05ac11ff |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bpiyush/CLIP_prefix_caption-video | MultiHeadAttention | false | 12,192 | [
"MIT"
] | 0 | 3f6a4b8c841189e20b82fd4de127681424311599 | https://github.com/bpiyush/CLIP_prefix_caption-video/tree/3f6a4b8c841189e20b82fd4de127681424311599 |
CCAMDec | # 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.... | ruijieren98/DANet | CCAMDec | false | 16,359 | [
"MIT"
] | 2,190 | e38d61e371179833c08888fd5a1ee444cf5bd875 | https://github.com/ruijieren98/DANet/tree/e38d61e371179833c08888fd5a1ee444cf5bd875 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertLayerNorm(nn.Module):
def __init__(self, config, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GingerNg/SDNet | BertAttention | false | 16,213 | [
"MIT"
] | 112 | 48ad8cc57c9a02aaad10e34d0c91a174ac68f056 | https://github.com/GingerNg/SDNet/tree/48ad8cc57c9a02aaad10e34d0c91a174ac68f056 |
AdaptiveCatAvgMaxPool2d | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... | 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 torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.functi... | Alibaba-MIIL/ZS_SDL | AdaptiveCatAvgMaxPool2d | false | 8,041 | [
"MIT"
] | 20 | 769fe4f57d2d458a7c4b5468a6395c9b296b1dad | https://github.com/Alibaba-MIIL/ZS_SDL/tree/769fe4f57d2d458a7c4b5468a6395c9b296b1dad |
RZTXDecoderLayer | # 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.... | mpariente/rezero | RZTXDecoderLayer | false | 16,125 | [
"MIT"
] | 376 | 6bcf1df00bc9a3560b093a2bbe12dade92f86eba | https://github.com/mpariente/rezero/tree/6bcf1df00bc9a3560b093a2bbe12dade92f86eba |
TemperatureTanh | import torch
from torch import Tensor
from torch.functional import Tensor
from torch import nn as nn
class TemperatureTanh(nn.Module):
def __init__(self, temperature: 'float'=1.0) ->None:
"""The hyperbolic tangent with an optional temperature."""
super().__init__()
assert temperature != 0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Mingxiao-Li/vln-ce-eval | TemperatureTanh | false | 2,652 | [
"MIT"
] | 0 | 2217513e9d9b6352bf0939d3b76a359c64e89dda | https://github.com/Mingxiao-Li/vln-ce-eval/tree/2217513e9d9b6352bf0939d3b76a359c64e89dda |
TransferConv3 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.utils.data
class TransferConv3(nn.Module):
def __init__(self, n_channels, n_channels_in=None, residual=False):
super().__init__()
if n_channels_in is None:
n_channels_in = n_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | jozhang97/Side-tuning | TransferConv3 | false | 15,731 | [
"MIT"
] | 56 | dea345691fb7ee0230150fe56ddd644efdffa6ac | https://github.com/jozhang97/Side-tuning/tree/dea345691fb7ee0230150fe56ddd644efdffa6ac |
SimpleExpModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | briancoutinho/glow | SimpleExpModule | false | 12,566 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
WassersteinGeneratorLoss | # 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
import torch.autograd
import torch.utils.data
assert_size_stride = ... | kayuksel/torchgan | WassersteinGeneratorLoss | false | 10,552 | [
"MIT"
] | 0 | 739d97cef4c49fb80155de84e609471efafab107 | https://github.com/kayuksel/torchgan/tree/739d97cef4c49fb80155de84e609471efafab107 |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CookiePPP/mellotron | MultiHeadAttention | false | 9,062 | [
"BSD-3-Clause"
] | 0 | 488425981c19cd0eddddea13d1348da4bfef8d26 | https://github.com/CookiePPP/mellotron/tree/488425981c19cd0eddddea13d1348da4bfef8d26 |
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