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 |
|---|---|---|---|---|---|---|---|---|---|---|
CausalConv2d | # 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... | sajjad2014/vq-vae-2-pytorch | CausalConv2d | false | 16,356 | [
"MIT"
] | 1,007 | ef5f67c46f93624163776caec9e0d95063910eca | https://github.com/sajjad2014/vq-vae-2-pytorch/tree/ef5f67c46f93624163776caec9e0d95063910eca |
KLDivLossWithLogits | import torch
import torch.utils.data
import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class AbstractConsistencyLoss(nn.Module):
def __init__(self, reduction='mean'):
super().__init__()
self.reduction = reduction
def for... | 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... | lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS | KLDivLossWithLogits | false | 12,731 | [
"MIT"
] | 0 | 132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 | https://github.com/lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS/tree/132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 |
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 import triton_helpers
from torch._inductor.runtime.... | malhotraa/transformer-experiments | Block | false | 10,472 | [
"MIT"
] | 0 | 82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | https://github.com/malhotraa/transformer-experiments/tree/82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f |
SoftDiceLossSquared | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class SoftDiceLossSquared(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
smooth=1.0):
"""
squares the terms in the denominator as proposed by Milletari et 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | dkswxd/Swin-Transformer-Semantic-Segmentation | SoftDiceLossSquared | false | 1,851 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 |
SpeakNet | import math
import torch
import torch.nn as nn
import torch.optim
def xavier_init(module):
"""
Xavier initializer for module parameters.
"""
for parameter in module.parameters():
if len(parameter.data.shape) == 1:
parameter.data.fill_(0)
else:
fan_in = parameter... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | christiancosgrove/cs767hw3 | SpeakNet | false | 9,905 | [
"MIT"
] | 0 | 7c906d7b92394cc30ed94a714b199467c269cadf | https://github.com/christiancosgrove/cs767hw3/tree/7c906d7b92394cc30ed94a714b199467c269cadf |
Accuracy | import torch
import torch.nn as nn
class Accuracy(nn.Module):
def __init__(self, threshold=0.5):
super().__init__()
self.threshold = threshold
def forward(self, y_true, y_pred):
preds = (y_pred > self.threshold).int()
return (preds == y_true).sum().float() / len(preds)
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | alessandroferrari/defeatcovid19-net-pytorch | Accuracy | false | 18,239 | [
"MIT"
] | 9 | fe9ed82563709bae92524093c3bc0bb887fbdf6d | https://github.com/alessandroferrari/defeatcovid19-net-pytorch/tree/fe9ed82563709bae92524093c3bc0bb887fbdf6d |
BothContextGate | # 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 ... | AngusGLChen/qg | BothContextGate | false | 4,870 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
"""Normalize nn.Module. As at pytorch, simplified"""
def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225
], inplace=False, dtype=torch.float32):
super().__init__()
mean = torch.as_tensor(mean, dtype=dty... | 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... | ayasyrev/pt_utils | Normalize | false | 1,503 | [
"Apache-2.0"
] | 0 | cb29b8fb4a3981248e1055979cc773f719dccdc7 | https://github.com/ayasyrev/pt_utils/tree/cb29b8fb4a3981248e1055979cc773f719dccdc7 |
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.... | ChantalMP/Graphormer | EncoderLayer | false | 8,968 | [
"MIT"
] | 0 | 5c384d0f2840afc88ee88aeb874f4b1f41d760bf | https://github.com/ChantalMP/Graphormer/tree/5c384d0f2840afc88ee88aeb874f4b1f41d760bf |
MarginLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class MarginLoss(Module):
'\n ## Margin loss for class existence\n\n A separate margin loss is used for each output capsule and the total loss is the sum of them.... | 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.nn import Module
... | ppvalluri09/annotated_deep_learning_paper_implementations | MarginLoss | false | 11,079 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
Zeronet | import torch
import torch.nn as nn
class Zeronet(nn.Module):
def forward(self, x):
"""
Return a zero-out copy of x
:param x: torch.Tensor
:return: x*0, type torch.Tensor
"""
return torch.zeros_like(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | hedixia/xhd_source | Zeronet | false | 6,791 | [
"MIT"
] | 1 | cb176bceb5f5349d68206aaf60014e251de36300 | https://github.com/hedixia/xhd_source/tree/cb176bceb5f5349d68206aaf60014e251de36300 |
ContrastivePairwiseEmbeddingLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.multiprocessing
import torch.backends
class ContrastivePairwiseEmbeddingLoss... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dokholyan/catalyst | ContrastivePairwiseEmbeddingLoss | false | 378 | [
"Apache-2.0"
] | 0 | de8e681676d76741fdb722d4cd77274ba616915d | https://github.com/Dokholyan/catalyst/tree/de8e681676d76741fdb722d4cd77274ba616915d |
NetVLAD | # 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.... | glee1228/segment_temporal_context_aggregation | NetVLAD | false | 6,760 | [
"Apache-2.0"
] | 1 | e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d | https://github.com/glee1228/segment_temporal_context_aggregation/tree/e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgP... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Kumaken/fyp-vehicle-counting-system | Classify | false | 2,473 | [
"MIT"
] | 0 | 51adb3bfc762d5489bc643da5f79bec3fa9eeb84 | https://github.com/Kumaken/fyp-vehicle-counting-system/tree/51adb3bfc762d5489bc643da5f79bec3fa9eeb84 |
NatureHead | # 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_... | andy920262/pytorch-a2c-ppo-acktr | NatureHead | false | 12,095 | [
"MIT"
] | 0 | 2e7e85219dfe737cb4036de3cf0c8b00706d640e | https://github.com/andy920262/pytorch-a2c-ppo-acktr/tree/2e7e85219dfe737cb4036de3cf0c8b00706d640e |
mbr_convex_hull | import torch
import torch.nn as nn
class mbr_convex_hull(nn.Module):
"""
Miminum Bounding Rectangle (MBR)
Algorithm core: The orientation of the MBR is the same as the one of one of the edges of the point cloud convex hull, which means
the result rectangle must overlap with at least one of... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | liuhuaijjin/rpn_rois_proposals_layers | mbr_convex_hull | false | 7,122 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
RobertaClassificationHead | # 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 ... | Hzfinfdu/Black-Box-Tuning | RobertaClassificationHead | false | 4,733 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
L1Loss | # 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
... | ZephyrII/mmpose_charger | L1Loss | false | 12,039 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
ReduceSum | import torch
import torch.onnx
import torch.nn as nn
class ReduceSum(nn.Module):
def forward(self, x):
return torch.sum(x, -1, keepdim=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | ReduceSum | false | 16,079 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
ToMono | import torch
import torch.nn as nn
class ToMono(nn.Module):
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return torch.mean(waveform, dim=0, keepdim=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | icyda17/very-deep-CNNs | ToMono | false | 10,216 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
RMulFloat | # 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... | Akababa/torch2trt | RMulFloat | false | 18,402 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
MNISTClassifier | import torch
import torchvision
import torchvision.ops
from torch import nn
class DeformableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=False):
super(DeformableConv2d, self).__init__()
assert type(kernel_size) == tuple or type(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torchvision
import tor... | developer0hye/PyTorch-Deformable-Convolution-v2 | MNISTClassifier | false | 15,194 | [
"MIT"
] | 70 | 3ed601fa70ee111278b95b134caf29e085642bc2 | https://github.com/developer0hye/PyTorch-Deformable-Convolution-v2/tree/3ed601fa70ee111278b95b134caf29e085642bc2 |
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
import math
import torch.nn ... | Jh-SYSU/MolRep | ScaleNorm | false | 13,882 | [
"MIT"
] | 57 | b2c802d18d41d7db26c19c6dd644098f945e48a1 | https://github.com/Jh-SYSU/MolRep/tree/b2c802d18d41d7db26c19c6dd644098f945e48a1 |
ClassificationModel | import torch
from torch import nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DerekGloudemans/temporary-repo | ClassificationModel | false | 5,101 | [
"MIT"
] | 1 | f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad | https://github.com/DerekGloudemans/temporary-repo/tree/f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad |
LuongAttentionDot | # 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.... | beroguedou/nmt-pytorch | LuongAttentionDot | false | 6,336 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
selfCrossEntropy | # 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
... | FizzerYu/CollaborativeVAE | selfCrossEntropy | false | 488 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
FFNNClassifier | from torch.nn import Module
import torch
from torch import FloatTensor
from torch.nn import Linear
from torch.nn.functional import tanh
from torch.nn.functional import log_softmax
from torch.autograd import Variable
class FFNNClassifier(Module):
def __init__(self, n_inputs, n_hidden, n_outputs):
super(FF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | theofpa/ci-torcs | FFNNClassifier | false | 4,424 | [
"MIT"
] | 0 | fcd1e9822301f1ad8f633468ed6276059afa94b9 | https://github.com/theofpa/ci-torcs/tree/fcd1e9822301f1ad8f633468ed6276059afa94b9 |
SplitAndConcat | # 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.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | ananthsub/d2go | SplitAndConcat | false | 18,318 | [
"Apache-2.0"
] | 3 | 8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 | https://github.com/ananthsub/d2go/tree/8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 |
HyperConv2d | # 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
import torch.nn.functional as F
import torch.utils.data
as... | Justin-Tan/ffjord | HyperConv2d | false | 698 | [
"MIT"
] | 0 | 2caf8a4ff84933672fe0d94255d665b3dd7a6791 | https://github.com/Justin-Tan/ffjord/tree/2caf8a4ff84933672fe0d94255d665b3dd7a6791 |
ActFirstResBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | MattAlexMiracle/SmartPatch | ActFirstResBlock | false | 17,707 | [
"MIT"
] | 7 | c485cb433d8e085d6eae10a335ee19f5e6c1a41c | https://github.com/MattAlexMiracle/SmartPatch/tree/c485cb433d8e085d6eae10a335ee19f5e6c1a41c |
Attention | import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
"""Implements additive attention and return the attention vector used to weight the values.
Additive attention consists in concatenating key and query and then passing them trough a linear layer."""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alpgokcek/turkish-qg-model | Attention | false | 3,103 | [
"MIT"
] | 0 | e90050d869958325aeaf639a2b1ff5eb2856e318 | https://github.com/alpgokcek/turkish-qg-model/tree/e90050d869958325aeaf639a2b1ff5eb2856e318 |
FThreshold | import random
import torch
import torch.nn as nn
class FThreshold(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FThreshold, self).__init__()
self.threshold = random.random()
self.value = self.threshold + random.random()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | FThreshold | false | 15,141 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
AlphaGoCnn | import torch
import torch.nn.functional as F
import torch.nn as nn
class AlphaGoCnn(nn.Module):
def __init__(self):
super(AlphaGoCnn, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 32, 3, 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... | Theomat/go-enseirb-2020 | AlphaGoCnn | false | 2,902 | [
"Apache-2.0"
] | 0 | ae842888dfd61a23d3556c5f63c4474bdbb1685f | https://github.com/Theomat/go-enseirb-2020/tree/ae842888dfd61a23d3556c5f63c4474bdbb1685f |
Alignment | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as f
class Module(nn.Module):
def __init__(self):
super().__init__()
self.summary = {}
def add_summary(self, name, val):
if self.trainin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Chriskuei/FedMatch | Alignment | false | 18,378 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
Transition | # 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.... | morimo27182/DeepKalmanFilter | Transition | false | 12,794 | [
"MIT"
] | 0 | 5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 | https://github.com/morimo27182/DeepKalmanFilter/tree/5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 |
_ResampleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torc... | Gian-Wiher/darts | _ResampleNorm | false | 5,208 | [
"Apache-2.0"
] | 1 | 0d267e08643e2e3f88163a5d955b8be75840c2f6 | https://github.com/Gian-Wiher/darts/tree/0d267e08643e2e3f88163a5d955b8be75840c2f6 |
SiameseMLP | import torch
from torch import nn
from torch.nn import functional as F
class SiameseMLP(nn.Module):
"""
basic structure similar to the MLP
input is splited into two 1*14*14 images for separating training, share the same parameters
"""
def __init__(self):
super(SiameseMLP, 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
from torch import nn
assert_s... | EE559DeepLearningEPFL/Project1 | SiameseMLP | false | 391 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
TargetContextGate | # 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 ... | LeeeeoLiu/OpenNMT-py | TargetContextGate | false | 2,508 | [
"MIT"
] | 0 | 9be3a8951e9181aabe5440e4ea98173b7e749b5c | https://github.com/LeeeeoLiu/OpenNMT-py/tree/9be3a8951e9181aabe5440e4ea98173b7e749b5c |
Actor | import torch
import torch.nn.functional as F
import torch.nn as nn
class Actor(nn.Module):
""" Neural Network for the Actor Model """
def __init__(self, state_size, action_size, max_action, seed=0,
layer1_units=400, layer2_units=300):
"""Initialize parameters and build model.
Params
=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | FranckNdame/drlkit | Actor | false | 8,137 | [
"MIT"
] | 33 | 698f3c182036cc5eed68f2a05b53a3e3670146bf | https://github.com/FranckNdame/drlkit/tree/698f3c182036cc5eed68f2a05b53a3e3670146bf |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 numpy as np
import tor... | YufengJin/deep-reinforcement-learning | Critic | false | 2,996 | [
"MIT"
] | 0 | 141cf00f169b46aa492c9e7520429bfdaab0117d | https://github.com/YufengJin/deep-reinforcement-learning/tree/141cf00f169b46aa492c9e7520429bfdaab0117d |
SAGEConv | # 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.... | sigeisler/grb | SAGEConv | false | 16,451 | [
"MIT"
] | 51 | c89e21076dc05d1edb87dfe2eff20c29ba6bd0c1 | https://github.com/sigeisler/grb/tree/c89e21076dc05d1edb87dfe2eff20c29ba6bd0c1 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class Discriminator(nn.Module):
def __init__(self, outputs_size, K=2):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(outputs_size, outputs_size // K, bias=True)
outputs_size = out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from sk... | Ulian7/DeepCTR | Discriminator | false | 1,184 | [
"Apache-2.0"
] | 0 | d8f519a722a4d6a4f1fe18e04af54cfd1369c9a5 | https://github.com/Ulian7/DeepCTR/tree/d8f519a722a4d6a4f1fe18e04af54cfd1369c9a5 |
ResidualAttentionBlock | import math
import torch
import torch as th
import torch.nn as nn
class LayerNorm(nn.LayerNorm):
"""
Implementation that supports fp16 inputs but fp32 gains/biases.
"""
def forward(self, x: 'th.Tensor'):
return super().forward(x.float())
class QKVMultiheadAttention(nn.Module):
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GastonMazzei/escher-project-website | ResidualAttentionBlock | false | 17,315 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
TwoLayerNet | # 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.quantization
imp... | harrydrippin/tutorials | TwoLayerNet | false | 12,482 | [
"BSD-3-Clause"
] | 0 | a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb | https://github.com/harrydrippin/tutorials/tree/a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb |
pair_norm | # 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... | ngohienduong/Deep_GCN_Benchmarking | pair_norm | false | 16,184 | [
"MIT"
] | 70 | 3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 | https://github.com/ngohienduong/Deep_GCN_Benchmarking/tree/3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 |
ReRegualizedLinearMNACLayer | import collections
import math
import torch
import torch.utils.data
def sparsity_error(W):
W_error = torch.min(torch.abs(W), torch.abs(1 - torch.abs(W)))
return torch.max(W_error)
def mnac(x, W, mode='prod'):
out_size, in_size = W.size()
x = x.view(x.size()[0], in_size, 1)
W = W.t().view(1, in_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import collections
import math
import torch.utils.data
assert_size_stride = torch._C._dyn... | hoedt/stable-nalu | ReRegualizedLinearMNACLayer | false | 3,614 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
EnvModel | # 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 ... | spacegoing/oc_hrl_pytorch | EnvModel | false | 13,068 | [
"MIT"
] | 0 | 3e6c3b32b41d7dad40a9ee35f436f8cbcde8633b | https://github.com/spacegoing/oc_hrl_pytorch/tree/3e6c3b32b41d7dad40a9ee35f436f8cbcde8633b |
AdaptiveInstanceNorm | import torch
import torch.nn as nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | KwonGihyun/DiagonalGAN | AdaptiveInstanceNorm | false | 8,437 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
Project3D | import torch
import torch.nn as nn
import torch.utils.data
class Project3D(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super(Project3D, self).__init__()
self.batch_size = batc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Uehwan/SimVODIS | Project3D | false | 14,525 | [
"MIT"
] | 117 | 288ae6f3bf37336f2c829b3a6371793990b23214 | https://github.com/Uehwan/SimVODIS/tree/288ae6f3bf37336f2c829b3a6371793990b23214 |
IndepAnisotropicGaussianUVLoss | # 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 math... | TinBacon/FastAutoAugmentation | IndepAnisotropicGaussianUVLoss | false | 5,892 | [
"Apache-2.0"
] | 1 | 011e4e348fd9a937a29df11695dc71410f555d0a | https://github.com/TinBacon/FastAutoAugmentation/tree/011e4e348fd9a937a29df11695dc71410f555d0a |
Squeezing | # 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... | hongyehu/NeuralRG | Squeezing | false | 15,542 | [
"Apache-2.0"
] | 65 | ff4eb18f7f9e083dac6f3da3995f3f69ecf381e8 | https://github.com/hongyehu/NeuralRG/tree/ff4eb18f7f9e083dac6f3da3995f3f69ecf381e8 |
MatrixTree | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:ci... | 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
import torch.cuda
import torch.distributed
assert_s... | MaxatTezekbayev/OpenNMT-py-lexical | MatrixTree | false | 5,613 | [
"MIT"
] | 1 | 44182999b863fc4074d67e0281c5bdab19abddfe | https://github.com/MaxatTezekbayev/OpenNMT-py-lexical/tree/44182999b863fc4074d67e0281c5bdab19abddfe |
PositionalEncoding | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | TheoMoutakanni/hcrn-videoqa | PositionalEncoding | false | 2,937 | [
"Apache-2.0"
] | 0 | 03a0fb1f24d756e7cd61d519f92925b610a91a29 | https://github.com/TheoMoutakanni/hcrn-videoqa/tree/03a0fb1f24d756e7cd61d519f92925b610a91a29 |
PA | import torch
import torch.nn as nn
class PA(nn.Module):
"""PA is pixel attention"""
def __init__(self, nf):
super(PA, self).__init__()
self.conv = nn.Conv2d(nf, nf, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y = self.conv(x)
y = self.sigmoid(y)
o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | YingqiLiulll/scrips_for_SR | PA | false | 1,262 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
Discriminator | # 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... | davide-belli/deep-learning-labs | Discriminator | false | 1,800 | [
"MIT"
] | 0 | 1acd37a527711dccdc00c1935724cc5de7c10955 | https://github.com/davide-belli/deep-learning-labs/tree/1acd37a527711dccdc00c1935724cc5de7c10955 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | AllenPu/mbdg | LayerNorm | false | 7,669 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
TemporalBlock | import torch
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
def TemporalConvLayer(input_channels, output_channels, kernel_size):
m = nn.Conv1d(in_channels=input_channels, out_channels=output_channels,
kernel_size=kernel_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | verashira/TSPNet | TemporalBlock | false | 16,671 | [
"MIT"
] | 83 | ee454165dcc61cdbbff19565364e2221727ed2b8 | https://github.com/verashira/TSPNet/tree/ee454165dcc61cdbbff19565364e2221727ed2b8 |
GCN | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.functional as F
import torch.nn.parallel
as... | XDong18/AdelaiDet | GCN | false | 12,062 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f |
Model | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=6, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(32, 32, kernel_size=6, stride=1, padding=1
)
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | stepan-krivanek/image-recognition | Model | false | 10,815 | [
"MIT"
] | 0 | 6c421e768e83db489e4caa22989f7dad95519578 | https://github.com/stepan-krivanek/image-recognition/tree/6c421e768e83db489e4caa22989f7dad95519578 |
GeneralizedMeanPooling | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules import Module
class GeneralizedMeanPooling(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)), ... | 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.nn import Module
... | ByungHeeCha/visual_localization | GeneralizedMeanPooling | false | 17,020 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc |
OcrPtrNet | import math
import torch
from torch import nn
class OcrPtrNet(nn.Module):
def __init__(self, hidden_size, query_key_size=None):
super().__init__()
if query_key_size is None:
query_key_size = hidden_size
self.hidden_size = hidden_size
self.query_key_size = query_key_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | junj2ejj/sam-textvqa | OcrPtrNet | false | 15,744 | [
"W3C"
] | 48 | 6bf646d741fb2536e3a8f331c78b594f6199df15 | https://github.com/junj2ejj/sam-textvqa/tree/6bf646d741fb2536e3a8f331c78b594f6199df15 |
SimpleXorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleXorModule(torch.nn.Module):
def __init__(self):
super(SimpleXorModule, self).__init__()
def forward(self, a, b):
c = torch.logical_xor(a, b)
return torch.logical_xor(c, c)
def get_inputs():
return [torc... | 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... | opti-mix/glow | SimpleXorModule | false | 7,416 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
FeatureResizer | import torch
import torch.utils.data
import torch
import torch.nn
import torch.optim
import torch.utils
from torch import nn
import torch.distributed
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a 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.utils.... | mmaaz60/mdetr | FeatureResizer | false | 10,485 | [
"Apache-2.0"
] | 0 | fe1394c67e76a6c7e521bbda77d8294714038a3a | https://github.com/mmaaz60/mdetr/tree/fe1394c67e76a6c7e521bbda77d8294714038a3a |
ConvWS2d | # 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 ... | BradleyBrown19/CustomObjectDetector | ConvWS2d | false | 2,090 | [
"Apache-2.0"
] | 0 | 11c14ec6127c553ac365703c768b75dde33d9a4d | https://github.com/BradleyBrown19/CustomObjectDetector/tree/11c14ec6127c553ac365703c768b75dde33d9a4d |
Softmax2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Yusoi/mmdetection | Softmax2d | false | 9,739 | [
"Apache-2.0"
] | 0 | cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a | https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a |
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.triton_helpers import libdevice
import torch.nn as ... | MarcoForte/DeepInteractiveSegmentation | Conv2d | false | 14,012 | [
"MIT"
] | 95 | ddd7426ea9f36ff6a110d836b1b920a1215cbfee | https://github.com/MarcoForte/DeepInteractiveSegmentation/tree/ddd7426ea9f36ff6a110d836b1b920a1215cbfee |
CausalConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | heyitsmine/FewRel | CausalConv1d | false | 10,255 | [
"MIT"
] | 0 | 2a2b8ae471298d9eb3557796a085c23b21982fb2 | https://github.com/heyitsmine/FewRel/tree/2a2b8ae471298d9eb3557796a085c23b21982fb2 |
Snake | import torch
import torch.nn as nn
class Snake(nn.Module):
""" Implementation of the snake activation function as a torch nn module
The result of the activation function a(x) is calculated by a(x) = x + sin^2(x)
With alpha is a trainab
"""
def __init__(self, frequency=10):
"""Constructor... | 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... | ComputationalRadiationPhysics/NeuralSolvers | Snake | false | 13,518 | [
"MIT"
] | 59 | cc62b5a91d9eb70ffafdcca6d1fbba16d3bf588d | https://github.com/ComputationalRadiationPhysics/NeuralSolvers/tree/cc62b5a91d9eb70ffafdcca6d1fbba16d3bf588d |
Wav2Vec2ClassificationHead | # 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 ... | Ayushk4/MedImaging | Wav2Vec2ClassificationHead | false | 1,871 | [
"MIT"
] | 0 | dbc8968f076385be0c8db42210817ae0940fa26a | https://github.com/Ayushk4/MedImaging/tree/dbc8968f076385be0c8db42210817ae0940fa26a |
PositionGenerator | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | nigelnnk/MATCh-sensitivity | PositionGenerator | false | 7,336 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
ToTensor | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | AlexMontgomerie/finn | ToTensor | false | 13,238 | [
"BSD-3-Clause"
] | 283 | ec5f67b333ad4db4acf6191c3b5ab5e9067347aa | https://github.com/AlexMontgomerie/finn/tree/ec5f67b333ad4db4acf6191c3b5ab5e9067347aa |
Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
assert_size_stride = torch._C._dynam... | NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio | Conv | false | 879 | [
"MIT"
] | 0 | 231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 | https://github.com/NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio/tree/231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 |
StyledConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | ShinoharaHare/stylegan2-pytorch | StyledConv | false | 2,839 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 5a4b1c4e9753681bc1694195f3b2391527c1b525 | https://github.com/ShinoharaHare/stylegan2-pytorch/tree/5a4b1c4e9753681bc1694195f3b2391527c1b525 |
DiceLoss | import torch
import torch.nn as nn
def binaray_dice_loss(predict, target, smooth=1, p=2, weight=None):
"""Dice loss for binary classification
Args:
predict(Tensor): a tensor of shape [N, H, W]
target(Tensor): a tensor of shape same with predict
smooth(float): a float number to smooth ... | 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... | hikopensource/DAVAR-Lab-OCR | DiceLoss | false | 15,518 | [
"Apache-2.0"
] | 387 | c65285f6668864cca7a12770ae4c8d083ea1cf1b | https://github.com/hikopensource/DAVAR-Lab-OCR/tree/c65285f6668864cca7a12770ae4c8d083ea1cf1b |
RefineLavaLampModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineLavaLampModel | false | 7,866 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
Conv1d | import torch
import torch.nn as nn
import torch.utils.data
class Conv1d(nn.Conv1d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param stride: Scalar
:param paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | CookiePPP/mellotron | Conv1d | false | 9,050 | [
"BSD-3-Clause"
] | 0 | 488425981c19cd0eddddea13d1348da4bfef8d26 | https://github.com/CookiePPP/mellotron/tree/488425981c19cd0eddddea13d1348da4bfef8d26 |
SEModule | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2d(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
from torch.nn import Module
f... | AsianZeus/Diverse-Facial-Edit | SEModule | false | 9,416 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
IBWDCT | import torch
import numpy as np
import torch.nn.parallel
import torch.utils.data
from torch import nn
import torch.fft
class IBWDCT(nn.Module):
def __init__(self):
super().__init__()
self.ibwdct = nn.ConvTranspose2d(64, 1, 8, 8, bias=False)
self.ibwdct.weight.requires_grad = False
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.nn.parallel
import torch.utils.data
from torch i... | KazutakaYamanouchi/bachelor-study | IBWDCT | false | 2,663 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
KL_loss | import torch
import torch.nn.functional
class KL_loss(torch.nn.Module):
def __init__(self):
super(KL_loss, self).__init__()
def forward(self, mu, logvar):
KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar
)
KLD = torch.sum(KLD_element).mul_(-0.5)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | KL_loss | false | 15,739 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
Symmetric | # 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... | vishalbelsare/geotorch | Symmetric | false | 16,675 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
FFNN1 | import torch
import torch.utils.data
from torch import nn
class FFNN1(nn.Module):
def __init__(self, input_size, hidden_size, hidden_dropout_prob):
super(FFNN1, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.hidden_dropout_prob = hidden_dropout_p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from ... | MaurizioFD/recsys-challenge-2020-twitter | FFNN1 | false | 8,522 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
Foo | import torch
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Foo(torch.nn.Module):
def __init__(self, size):
super(Foo, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn... | 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.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_si... | alexshuang/apex | Foo | false | 1,405 | [
"BSD-3-Clause"
] | 0 | 107f1ff569c40769de2ed8d366126282e63b63ce | https://github.com/alexshuang/apex/tree/107f1ff569c40769de2ed8d366126282e63b63ce |
Critic | # 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 ... | ctoto93/TD3 | Critic | false | 9,962 | [
"MIT"
] | 0 | 88482b9f1fb4441d74426ece60d5da13414aeb77 | https://github.com/ctoto93/TD3/tree/88482b9f1fb4441d74426ece60d5da13414aeb77 |
EncoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
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.... | AlbertiPot/attention-is-all-you-need-pytorch | EncoderLayer | false | 37 | [
"MIT"
] | 0 | c5ec40907db281b85b3bd7a5dd8016940291add0 | https://github.com/AlbertiPot/attention-is-all-you-need-pytorch/tree/c5ec40907db281b85b3bd7a5dd8016940291add0 |
LayerScale | # 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... | DilwoarH/demucs | LayerScale | false | 5,075 | [
"MIT"
] | 1 | 32d21592dfa015468aa117cace52b21e7af79d71 | https://github.com/DilwoarH/demucs/tree/32d21592dfa015468aa117cace52b21e7af79d71 |
VGG19Decoder2 | # 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.... | chenhsiu48/PytorchWCT | VGG19Decoder2 | false | 9,936 | [
"MIT"
] | 0 | c3346ebaec95358ad1d4d5a519d5d0e7de73bc75 | https://github.com/chenhsiu48/PytorchWCT/tree/c3346ebaec95358ad1d4d5a519d5d0e7de73bc75 |
HighLightLayer | # 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.parallel
import torch.nn as nn
import torch.utils.data
import to... | EGO4D/episodic-memory | HighLightLayer | false | 8,084 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
G_Small | # 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_... | RQuispeC/pytorch-ACSCP | G_Small | false | 8,762 | [
"MIT"
] | 25 | c83f08632012c2245250ff9c5140814461db575c | https://github.com/RQuispeC/pytorch-ACSCP/tree/c83f08632012c2245250ff9c5140814461db575c |
GatedConv | # 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
import torch.utils.data
assert_size_stride = torch._C._dyn... | D-hash-code/ffjord | GatedConv | false | 11,362 | [
"MIT"
] | 0 | 3647ab35537a8bac3b4dc1e45a593819ac8e2c18 | https://github.com/D-hash-code/ffjord/tree/3647ab35537a8bac3b4dc1e45a593819ac8e2c18 |
MaskedMHCA | # 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.... | yjh0410/actionformer_release | MaskedMHCA | false | 16,801 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 |
Highway | # 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... | DennisMagnusson/voice2voice | Highway | false | 2,154 | [
"MIT"
] | 0 | cee95b3eda8c2159f6b85e1733652ff8b7a537ce | https://github.com/DennisMagnusson/voice2voice/tree/cee95b3eda8c2159f6b85e1733652ff8b7a537ce |
my_Layernorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | MAZiqing/FEDformer | my_Layernorm | false | 17,651 | [
"MIT"
] | 7 | 7914d39df829494a8172afb9676982c3789d491d | https://github.com/MAZiqing/FEDformer/tree/7914d39df829494a8172afb9676982c3789d491d |
BertOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=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.triton_helpers import libdevice
from torch import n... | Splendon/examples | BertOutput | false | 4,746 | [
"MIT"
] | 0 | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | https://github.com/Splendon/examples/tree/ed4a8a01857b6ddca49559141acf5d0986eb01e1 |
LSoftLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class LSoftLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_pred, y_true, beta):
with torch.no_grad():
y_true_updated = beta * y_true + (1 - beta) * y_pred
return F.binary_cross_... | 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... | advian123/kaggle-birdsong-recognition | LSoftLoss | false | 9,933 | [
"MIT"
] | 0 | a4ca8ab81e166b919452fb5d6ca4c2912c65e904 | https://github.com/advian123/kaggle-birdsong-recognition/tree/a4ca8ab81e166b919452fb5d6ca4c2912c65e904 |
InvConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = t... | hologerry/glow-pytorch-1 | InvConv2d | false | 3,639 | [
"MIT"
] | 0 | 9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 | https://github.com/hologerry/glow-pytorch-1/tree/9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 |
TanhDeepLiftModel | import torch
import torch.nn as nn
class TanhDeepLiftModel(nn.Module):
"""
Same as the ReLUDeepLiftModel, but with activations
that can have negative outputs
"""
def __init__(self):
super().__init__()
self.tanh1 = nn.Tanh()
self.tanh2 = nn.Tanh()
def forward(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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ngduduong/captum | TanhDeepLiftModel | false | 4,078 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
ContextualCell | # 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... | xkp793003821/nas-segm-pytorch | ContextualCell | false | 13,112 | [
"BSD-2-Clause"
] | 0 | c4b59ab56bd539bf08493c6d85072849213a3d62 | https://github.com/xkp793003821/nas-segm-pytorch/tree/c4b59ab56bd539bf08493c6d85072849213a3d62 |
PrototypicalNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.parallel
def L2SquareDist(A, B, average=True):
assert A.dim() == 3
assert B.dim() == 3
assert A.size(0) == B.size(0) and A.size(2) == B.size(2)
nB = A.size(0)
Na = A.size(1)
Nb =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_st... | nikran1/Few_shot | PrototypicalNetwork | false | 16,185 | [
"MIT"
] | 497 | 5298c98e208411e44ee7767e6f4d457006d373cb | https://github.com/nikran1/Few_shot/tree/5298c98e208411e44ee7767e6f4d457006d373cb |
LinearCombine | # 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 math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch.... | pkuyym/nni | LinearCombine | false | 10,999 | [
"MIT"
] | 0 | fe533e3bc65ea27997e16250adb503638548d500 | https://github.com/pkuyym/nni/tree/fe533e3bc65ea27997e16250adb503638548d500 |
DirectMultiheadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class DirectMultiheadAttention(nn.Module):
def __init__(self, d_in, d_out, heads, dropout=0.1):
super(DirectMultiheadAttention, self).__init__()
self.heads = heads
self.proj_pair = nn.Linear(d_in, heads)
self.drop ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | wukevin/RoseTTAFold | DirectMultiheadAttention | false | 4,560 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
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