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 |
|---|---|---|---|---|---|---|---|---|---|---|
MaxMarginRankingLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch as th
import torch.optim
import torch.utils.data
class MaxMarginRankingLoss(nn.Module):
def __init__(self, margin=1):
super(MaxMarginRankingLoss, self).__init__()
self.loss = th.nn.MarginRankingLoss(margin)
se... | 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 as th
import torch.optim
import torch.utils.data
asser... | awesome-archive/Video-to-Online-Platform | MaxMarginRankingLoss | false | 6,287 | [
"Apache-2.0"
] | 1 | 4f91724133a817e79bce91e0abbd46cf38a31167 | https://github.com/awesome-archive/Video-to-Online-Platform/tree/4f91724133a817e79bce91e0abbd46cf38a31167 |
LinearBlock | # 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 functools import partial... | derwind/dmfont | LinearBlock | false | 15,173 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
JaccardScore | # 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | BloodAxe/segmentation-networks-benchmark | JaccardScore | false | 7,858 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
Polynomial3 | import torch
class Polynomial3(torch.nn.Module):
def __init__(self):
"""
In the constructor we instantiate four parameters and assign them as member parameters.
"""
super(Polynomial3, self).__init__()
self.a = torch.nn.Parameter(torch.randn(()))
self.b = torch.nn.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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | LbsIrving/PyTorch | Polynomial3 | false | 766 | [
"MIT"
] | 0 | 314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 | https://github.com/LbsIrving/PyTorch/tree/314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 |
ResidualBlock | import torch
import torch.nn as nn
def conv3x3(in_ch, out_ch, stride=1):
"""3x3 convolution with padding."""
return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)
class ResidualBlock(nn.Module):
"""Simple residual block with two 3x3 convolutions.
Args:
in_ch (int): number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Chrisa142857/CompressAI | ResidualBlock | false | 13,503 | [
"Apache-2.0"
] | 62 | 75760096b9700a58d346351251d544050f3418fb | https://github.com/Chrisa142857/CompressAI/tree/75760096b9700a58d346351251d544050f3418fb |
Anomaly | import torch
import torch.utils.data
from torch import nn
class Anomaly(nn.Module):
def __init__(self, window=1024):
self.window = window
super(Anomaly, self).__init__()
self.layer1 = nn.Conv1d(window, window, kernel_size=1, stride=1,
padding=0)
self.layer2 = nn.Conv1d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | sccc19/anomalydetector | Anomaly | false | 16,440 | [
"MIT"
] | 180 | a963ef8d7f30971e99d21a748d059e26f2163b09 | https://github.com/sccc19/anomalydetector/tree/a963ef8d7f30971e99d21a748d059e26f2163b09 |
Sinkhorn | # 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.utils.data
import torch.nn as nn
from sklearn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_st... | CityU-AIM-Group/SIGMA | Sinkhorn | false | 17,461 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 |
AlbertAttention | # 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.... | jxhe/unify-parameter-efficient-tuning | AlbertAttention | false | 15,784 | [
"Apache-2.0"
] | 101 | 3222ce2c0079566a28043e22380eb4ab6ad14389 | https://github.com/jxhe/unify-parameter-efficient-tuning/tree/3222ce2c0079566a28043e22380eb4ab6ad14389 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, input_dim, feature_dim):
super(Attention, self).__init__()
self.feature_dim = feature_dim
self.input_dim = input_dim
weight = torch.zeros(self.feature_dim, self.feature_dim)
nn.init.kaiming_u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ForoughA/CORGI | Attention | false | 8,104 | [
"MIT"
] | 22 | c28ecd0e0375569f9f05e94e6ae5b7a994caacf5 | https://github.com/ForoughA/CORGI/tree/c28ecd0e0375569f9f05e94e6ae5b7a994caacf5 |
TensorRepeat | import torch
class TensorRepeat(torch.nn.Module):
"""
duolicate a 1D tensor into N channels (grayscale to rgb for instance)
code derived from https://github.com/pytorch/vision/blob/main/torchvision/transforms/transforms.py
"""
def __init__(self, num_output_channels=1):
super().__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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | georand/distributedpytorch | TensorRepeat | false | 10,063 | [
"MIT"
] | 0 | 69341b364830ad62968ea5646e485dff6b0b24f2 | https://github.com/georand/distributedpytorch/tree/69341b364830ad62968ea5646e485dff6b0b24f2 |
DecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jkimbf/transformer-1 | DecoderLayer | false | 15,733 | [
"Apache-2.0"
] | 233 | 6cd29731197822d6db641cdbfad3b045b8a294e4 | https://github.com/jkimbf/transformer-1/tree/6cd29731197822d6db641cdbfad3b045b8a294e4 |
PPReLU | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IrisDinge/YoloV3_DOTA | PPReLU | false | 5,357 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
SigmoidRange | import torch
import torch.nn as nn
from typing import *
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class SigmoidRange(nn.Module):
"""Sigmoid module with range `(low, high)`"""
def __init__(self, low, high):
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
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | LaurenSpiegel/fastai_docs | SigmoidRange | false | 757 | [
"Apache-2.0"
] | 0 | 4fe6b62116d88dea9610548133e6cadb6b260a73 | https://github.com/LaurenSpiegel/fastai_docs/tree/4fe6b62116d88dea9610548133e6cadb6b260a73 |
FusedConvBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | youkaichao/tutorials | FusedConvBN | false | 13,202 | [
"BSD-3-Clause"
] | 0 | af34b10b70d99659eb016a2a1d5c31b9ae8ba3da | https://github.com/youkaichao/tutorials/tree/af34b10b70d99659eb016a2a1d5c31b9ae8ba3da |
ChamferLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
class ChamferLoss(nn.Module):
def __init__(self):
super(ChamferLoss, self).__init__()
self.use_cuda = torch.cuda.is_available()
def forward(self, preds, gts):
P = self.batch_pairwis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | BossunWang/soft-intro-vae-pytorch | ChamferLoss | false | 13,806 | [
"Apache-2.0"
] | 144 | 10841fe2ae1aea12dbf43347dea63ee25d951864 | https://github.com/BossunWang/soft-intro-vae-pytorch/tree/10841fe2ae1aea12dbf43347dea63ee25d951864 |
SmoothL1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
class SmoothL1Loss(nn.Module):
"""SmoothL1Loss loss .
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the los... | 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 | SmoothL1Loss | false | 12,012 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
SuperpointDescriptor | # 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_... | wx-b/SOLD2 | SuperpointDescriptor | false | 16,744 | [
"MIT"
] | 347 | 71c3243f9d3a695788d0a6bfd134b9849425900a | https://github.com/wx-b/SOLD2/tree/71c3243f9d3a695788d0a6bfd134b9849425900a |
JaccardLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.gu... | cxz/tgs-salt-identification-challenge | JaccardLoss | false | 6,515 | [
"MIT"
] | 1 | 859f3d7f2d3184532c42c34444500eec3b03b1c8 | https://github.com/cxz/tgs-salt-identification-challenge/tree/859f3d7f2d3184532c42c34444500eec3b03b1c8 |
Caps_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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | WdBlink/AugMix-3DOCUNet-Brats2019 | Caps_Conv | false | 5,960 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
RingLoss | # 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 warnings
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gua... | ArronHZG/ABD-Net | RingLoss | false | 9,588 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
PKT | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | UBCDingXin/RepDistiller | PKT | false | 14,519 | [
"BSD-2-Clause"
] | 1,347 | dcc043277f2820efafd679ffb82b8e8195b7e222 | https://github.com/UBCDingXin/RepDistiller/tree/dcc043277f2820efafd679ffb82b8e8195b7e222 |
RSubFloat | import torch
class RSubFloat(torch.nn.Module):
def __init__(self):
super(RSubFloat, self).__init__()
def forward(self, x):
return 1.0 - 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... | NVIDIA-AI-IOT-private/torch2trt | RSubFloat | false | 10,531 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
UpConv_Blocks | # 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_... | HRHLALALA/GoalGAN | UpConv_Blocks | false | 9,103 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 |
WeightedView | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
class WeightedView(nn.Module):
"""Calculate weighted view
Args:
num_groups: int, number of groups (views)
reduce_dimension: bool, default False. If True, reduce dimension dim
dim: default -1. Only used 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.optim
import torch.utils.data
assert_s... | BeautyOfWeb/AffinityNet | WeightedView | false | 7,763 | [
"MIT"
] | 34 | d3f79823fa0182328894483165d4f0853740ee53 | https://github.com/BeautyOfWeb/AffinityNet/tree/d3f79823fa0182328894483165d4f0853740ee53 |
LxmertAttention | # 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.... | ashutoshbsathe/SmBop | LxmertAttention | false | 9,813 | [
"MIT"
] | 0 | ce5f67ec070df55b84d7f3617659011732020c96 | https://github.com/ashutoshbsathe/SmBop/tree/ce5f67ec070df55b84d7f3617659011732020c96 |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Parskatt/mmdetection | NormedLinear | false | 927 | [
"Apache-2.0"
] | 0 | ee4cfa29e7f479b2454b1f1355f8c05be62d8466 | https://github.com/Parskatt/mmdetection/tree/ee4cfa29e7f479b2454b1f1355f8c05be62d8466 |
TanhGaussianPolicy | # 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 typing import List
from ... | redvinaa/multiagent-path-finding-continuous | TanhGaussianPolicy | false | 10,722 | [
"MIT"
] | 0 | 2d4ba3388f9b951c443ba72a33bd7af4f461275f | https://github.com/redvinaa/multiagent-path-finding-continuous/tree/2d4ba3388f9b951c443ba72a33bd7af4f461275f |
Ternary | # 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Ternary | false | 17,137 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
SDSDRLoss | import torch
def apply_reduction(losses, reduction='none'):
"""Apply reduction to collection of losses."""
if reduction == 'mean':
losses = losses.mean()
elif reduction == 'sum':
losses = losses.sum()
return losses
class SDSDRLoss(torch.nn.Module):
"""Scale-dependent signal-to-di... | 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... | leoauri/auraloss | SDSDRLoss | false | 15,902 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
WNConv2d | import torch
from torch import nn
import torch.utils.data
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_channel,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Shivanshu-Gupta/KaoKore-VQ-VAE2 | WNConv2d | false | 1,093 | [
"MIT"
] | 0 | 38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c | https://github.com/Shivanshu-Gupta/KaoKore-VQ-VAE2/tree/38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c |
Block | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Junhojuno/vision-transformer | Block | false | 5,440 | [
"MIT"
] | 1 | 38f8a17967e91e98f767c8e5754081ee8bcd72b4 | https://github.com/Junhojuno/vision-transformer/tree/38f8a17967e91e98f767c8e5754081ee8bcd72b4 |
DoubleResolutionLayer | import torch
import torch.nn as nn
class DoubleResolutionLayer(nn.Module):
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
return 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BeningSobariah/ark-stroller | DoubleResolutionLayer | false | 11,236 | [
"Apache-2.0"
] | 0 | af2036a1726523d5aca9b1040bfc1fad5c3420f2 | https://github.com/BeningSobariah/ark-stroller/tree/af2036a1726523d5aca9b1040bfc1fad5c3420f2 |
Dense | import torch
import numpy as np
import torch.nn as nn
def get_einsum_string(ndims, einsum_symbols=None):
if einsum_symbols is None:
einsum_symbols = ['u', 'v', 'w', 'x', 'y', 'z']
assert ndims <= len(einsum_symbols)
einsum_prefix = ''
for i in range(ndims):
einsum_prefix += einsum_symb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.g... | yyht/Funnel_Transformer | Dense | false | 16,781 | [
"MIT"
] | 193 | 4b35a794d5e122a8054471863a52d4eac1c39dcd | https://github.com/yyht/Funnel_Transformer/tree/4b35a794d5e122a8054471863a52d4eac1c39dcd |
ClassWisePool | # 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... | Ecocytus/Roberta-ZeroShot-Label | ClassWisePool | false | 5,097 | [
"MIT"
] | 1 | 8a6d74187a0e2fd5b1b75549cfb724f54269c5a5 | https://github.com/Ecocytus/Roberta-ZeroShot-Label/tree/8a6d74187a0e2fd5b1b75549cfb724f54269c5a5 |
PolicyBasis | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Sha-Lab/SynPo | PolicyBasis | false | 8,747 | [
"MIT"
] | 18 | 8ac35a01d2c810187b9c14b914bcb792ed73caa9 | https://github.com/Sha-Lab/SynPo/tree/8ac35a01d2c810187b9c14b914bcb792ed73caa9 |
BasicModel4_MultiArgs | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YNNEKUW/captum | BasicModel4_MultiArgs | false | 11,983 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
Masked_MSE_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | bg459/gan-ensembling-loader | Masked_MSE_Loss | false | 14,949 | [
"MIT"
] | 86 | 5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 | https://github.com/bg459/gan-ensembling-loader/tree/5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 |
SelfAttention | import math
import torch
import torch.nn as nn
from torch.nn import init
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if init_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
from torch._inductor.runtime.... | fanyix/flownet2 | SelfAttention | false | 6,698 | [
"Apache-2.0"
] | 1 | 0643beef59eeaf4cf4907d0d51f486ffd713363f | https://github.com/fanyix/flownet2/tree/0643beef59eeaf4cf4907d0d51f486ffd713363f |
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.... | mingweima/hintplaygame | Attention | false | 4,013 | [
"MIT"
] | 0 | 31f35a22111a2e5e7e5d8e90f92326bc784c5fe7 | https://github.com/mingweima/hintplaygame/tree/31f35a22111a2e5e7e5d8e90f92326bc784c5fe7 |
TransformerLayer | import math
import torch
import uuid
from torch import Tensor
import torch.nn as nn
from typing import Tuple
import torch.nn.functional as F
from typing import Optional
from typing import Dict
from torch.nn import Parameter
def gelu(x):
"""Implementation of the gelu activation function.
For information: Open... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | William-Zhanng/Protein_affinity | TransformerLayer | false | 6,018 | [
"MIT"
] | 1 | 8abd12073b182274bf464ff23fd3be406c4e39ac | https://github.com/William-Zhanng/Protein_affinity/tree/8abd12073b182274bf464ff23fd3be406c4e39ac |
WNConv2d | # 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 ... | imatge-upc/pixelcoordEDL | WNConv2d | false | 6,873 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
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 torch.utils.model_zoo
import torch.nn.parallel
import torch.optim
import... | HelenR6/imagenet-r | GELU | false | 13,758 | [
"MIT"
] | 155 | 0bf04f2bf5d60d1098fc9a78f4e8c042e434eb69 | https://github.com/HelenR6/imagenet-r/tree/0bf04f2bf5d60d1098fc9a78f4e8c042e434eb69 |
Arc2 | from torch.nn import Module
import torch
import numpy as np
class Arc2(Module):
def __init__(self, num_bends):
super(Arc2, self).__init__()
self.num_bends = num_bends
self.register_buffer('range', torch.arange(0, float(num_bends)))
def a(self, t):
return torch.cos(np.pi * 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 math as tl_math
from torch.nn import Module
import numpy as np
assert_size_stride = torch... | avecplezir/dnn-mode-connectivity | Arc2 | false | 1,502 | [
"BSD-2-Clause"
] | 0 | 9a92ca370571f542b33060f637239172a0d08bba | https://github.com/avecplezir/dnn-mode-connectivity/tree/9a92ca370571f542b33060f637239172a0d08bba |
Downsample | # 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.model_zoo
assert_size_stride = torch._C... | KamilDeja/guided-diffusion | Downsample | false | 2,448 | [
"MIT"
] | 0 | d0eeeb4637379a3ece40c4dd38ccdf5d8ed5e837 | https://github.com/KamilDeja/guided-diffusion/tree/d0eeeb4637379a3ece40c4dd38ccdf5d8ed5e837 |
MaskUpdate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DLwbm123/LBAM_inpainting | MaskUpdate | false | 17,192 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
OutputLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abhirajtiwari/QANet | OutputLayer | false | 18,227 | [
"MIT"
] | 4 | 85e1db4edf0710169268a091e7d7959e524f1ceb | https://github.com/abhirajtiwari/QANet/tree/85e1db4edf0710169268a091e7d7959e524f1ceb |
StochasticGate | import torch
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class StochasticGate(nn.Module):
"""Stochastically merges features from two levels
with varying size of the receptive field
"""
def __init__(self):
super(StochasticGate, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | candacelax/1-stage-wseg | StochasticGate | false | 3,294 | [
"Apache-2.0"
] | 0 | 7a24791a3a78454e6611399ba55a808491551543 | https://github.com/candacelax/1-stage-wseg/tree/7a24791a3a78454e6611399ba55a808491551543 |
NB | import torch
class NB(torch.nn.Module):
"""
Yang Comment: Usage in forward:
x : Ground truth
mu: Prediction
theta: Another trainable parameter with shape=[xdim(number of count variables)],
simply initialize a nn.Parameter(torch.randn(xdim)) in the model
Be careful, we need the nega... | 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
assert_size... | SchubertLab/mvTCR | NB | false | 8,750 | [
"MIT"
] | 16 | d815749e24650f69ef68054e0078d490af91b71d | https://github.com/SchubertLab/mvTCR/tree/d815749e24650f69ef68054e0078d490af91b71d |
BlurPool2d | # 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.nn import *
import... | aktgpt/brevis | BlurPool2d | false | 18,278 | [
"MIT"
] | 8 | 0c3dcabd241ea50cafbc2012250804e1ecb7555e | https://github.com/aktgpt/brevis/tree/0c3dcabd241ea50cafbc2012250804e1ecb7555e |
MLPTanH | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
class MLPTanH(nn.Module):
def __init__(self, input_dim, hidden_dim, vocab_size):
super(MLPTanH, self).__init__()
self.input_dim = input_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.triton_helpers import libdevice
import torch.nn as ... | kiathwe97/examples | MLPTanH | false | 10,353 | [
"BSD-3-Clause"
] | 0 | b4a8792023db8c50c7e9fb186bd982edd0dce3ce | https://github.com/kiathwe97/examples/tree/b4a8792023db8c50c7e9fb186bd982edd0dce3ce |
MLP | import random
import torch
import numpy as np
from torch import nn
class MLP(nn.Module):
def __init__(self, kernels, num_features, num_hiddens, normalize=True,
num_updates=3000, batch_size=128, weight_decay=0.0001, soft_preds=False
):
super().__init__()
self.kernels = kernels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | openmynet/tract | MLP | false | 12,860 | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 0 | a9aba6edcfeacd34f781f08717ae374bfbaba80e | https://github.com/openmynet/tract/tree/a9aba6edcfeacd34f781f08717ae374bfbaba80e |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self, N_STATES, N_ACTIONS):
super(Net, self).__init__()
self.fc1 = nn.Linear(N_STATES, 80)
self.fc1.weight.data.normal_(0, 0.1)
self.fc2 = nn.Linear(80, 60)
self.fc2.weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cariosr/States-Joeynmt | Net | false | 9,849 | [
"MIT"
] | 0 | 6b2eb67b990b586fe2bc4fb49004d749bc4f33be | https://github.com/cariosr/States-Joeynmt/tree/6b2eb67b990b586fe2bc4fb49004d749bc4f33be |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Manojbhat09/Sane-annotation-shape-complete | Critic | false | 17,701 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
BSConvU | # 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
assert_size_stride ... | YingqiLiulll/scrips_for_SR | BSConvU | false | 1,263 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
NoiseInjection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
import triton
import 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.... | delldu/StyleGAN2 | NoiseInjection | false | 6,559 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 1 | 4bcba4673d3dc32ac3a67f6b5d5e24b490cdfbb3 | https://github.com/delldu/StyleGAN2/tree/4bcba4673d3dc32ac3a67f6b5d5e24b490cdfbb3 |
BBoxTransform | import torch
from torch import nn
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, x2)]
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | awesome-amy/efficientmask | BBoxTransform | false | 12,139 | [
"MIT"
] | 0 | 2456d52af92f765de771fbb6bd27fe2b9f19533b | https://github.com/awesome-amy/efficientmask/tree/2456d52af92f765de771fbb6bd27fe2b9f19533b |
LossDice | import torch
import torch.nn as nn
class LossAbstract(nn.Module):
"""A named loss function, that loss functions should inherit from.
Args:
device (str): device key
"""
def __init__(self, device='cuda:0'):
super().__init__()
self.device = device
self.name = self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | MECLabTUDA/OOD-Gen | LossDice | false | 17,628 | [
"MIT"
] | 5 | f85ea9106ae1425f18e34c9d82fa3ca4925d8d9e | https://github.com/MECLabTUDA/OOD-Gen/tree/f85ea9106ae1425f18e34c9d82fa3ca4925d8d9e |
cross_entropy_prob | import torch
import torch.nn as nn
import torch.nn.functional as F
class cross_entropy_prob(nn.Module):
def __init__(self):
super(cross_entropy_prob, self).__init__()
def forward(self, pred, soft_targets):
pred = F.log_softmax(pred)
loss = torch.mean(torch.sum(-soft_targets * pred, 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | zwx8981/DBCNN-Pytorch | cross_entropy_prob | false | 16,830 | [
"MIT"
] | 150 | 16c3156054a30a3eabb45dffcf538f42452a14f3 | https://github.com/zwx8981/DBCNN-Pytorch/tree/16c3156054a30a3eabb45dffcf538f42452a14f3 |
FC | # 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.... | chenxi-wang/cs420-codes | FC | false | 6,435 | [
"MIT"
] | 1 | 756b71ea4f4d8c4694c8c3f32ed9d1c6e89fad15 | https://github.com/chenxi-wang/cs420-codes/tree/756b71ea4f4d8c4694c8c3f32ed9d1c6e89fad15 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, attn_dims):
super().__init__()
self.W = nn.Linear(attn_dims, attn_dims, bias=False)
self.v = nn.Linear(attn_dims, 1, bias=False)
def forward(self, encoder_seq_proj, query... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dacrol/WaveRNN-server | Attention | false | 11,328 | [
"MIT"
] | 0 | 5189829cec71938ff7ec2e3eb59e73af1382430a | https://github.com/Dacrol/WaveRNN-server/tree/5189829cec71938ff7ec2e3eb59e73af1382430a |
pg_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
from torch._inductor.runtime.... | anthonytec2/ssp-rl-final | pg_model | false | 3,122 | [
"MIT"
] | 0 | 4004678f7b820989d69824bd492307b3ed227b7a | https://github.com/anthonytec2/ssp-rl-final/tree/4004678f7b820989d69824bd492307b3ed227b7a |
LayerNorm | import torch
import torch.nn as nn
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class LayerNorm(nn.Module):
def __init__(self, hidden_size, epsilon, cast_fp16=True, formula=0):
super().__init__()
self.layer_norm = nn.LayerNorm(hidden_size, eps=epsilon)
self.layer_norm.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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn
import torch.onnx
import torch.utils.chec... | almiliMSFT/onnxruntime | LayerNorm | false | 14,802 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
FC_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class FC_Q(nn.Module):
def __init__(self, state_dim, num_actions):
super(FC_Q, self).__init__()
self.q1 = nn.Linear(state_dim, 256)
self.q2 = nn.Linear(256, 256)
self.q3 = nn.Linear(256, num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Thibaud-Ardoin/d4rl_evaluations | FC_Q | false | 14,499 | [
"Apache-2.0"
] | 123 | 135b23d3aecc234aacaeaaa019fbc7101d9b87ec | https://github.com/Thibaud-Ardoin/d4rl_evaluations/tree/135b23d3aecc234aacaeaaa019fbc7101d9b87ec |
VQVAEQuantize | import torch
from torch import nn
import torch.nn.functional as F
from scipy.cluster.vq import kmeans2
class VQVAEQuantize(nn.Module):
"""
Neural Discrete Representation Learning, van den Oord et al. 2017
https://arxiv.org/abs/1711.00937
Follows the original DeepMind implementation
https://github... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | crizCraig/deep-vector-quantization | VQVAEQuantize | false | 15,103 | [
"MIT"
] | 326 | c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 | https://github.com/crizCraig/deep-vector-quantization/tree/c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 |
ATLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
class ATLoss(nn.Module):
"""
Module for calculating AT Loss
:param norm_type (int): Norm to be used in calculating loss
"""
def __init__(self, norm_type=2):
super(ATLoss, self).__init__()
self.p = norm_type
... | 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
from t... | PiaCuk/KD_Lib | ATLoss | false | 14,184 | [
"MIT"
] | 360 | 153299d484e4c6b33793749709dbb0f33419f190 | https://github.com/PiaCuk/KD_Lib/tree/153299d484e4c6b33793749709dbb0f33419f190 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hzfinfdu/Black-Box-Tuning | BertAttention | false | 4,263 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
ChannelGate2d | # 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_... | bantiitnab/kaggle-TGS-salt-identification | ChannelGate2d | false | 1,520 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
SnakeHyperSolver | # 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.... | Juju-botu/diffeqml-research | SnakeHyperSolver | false | 13,929 | [
"Apache-2.0"
] | 49 | aa796c87447e5299ec4f25a07fc4d032afb1f63e | https://github.com/Juju-botu/diffeqml-research/tree/aa796c87447e5299ec4f25a07fc4d032afb1f63e |
DecoderBlock | # 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 ... | asmekal/catalyst | DecoderBlock | false | 12,120 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
SmoothL1Loss | # AOT ID: ['1_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.functi... | es6rc/icevision | SmoothL1Loss | false | 3,475 | [
"Apache-2.0"
] | 0 | bb78dd2e1721c2edb82fb9c1a826fe301541d2a1 | https://github.com/es6rc/icevision/tree/bb78dd2e1721c2edb82fb9c1a826fe301541d2a1 |
SymNetsCategoryLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KevinMusgrave/pytorch-adapt | SymNetsCategoryLoss | false | 13,964 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqawami/openvino_training_extensions | PositionwiseFeedForward | false | 15,234 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
LayerCake | # 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
assert_size_s... | dionhaefner/delve | LayerCake | false | 15,186 | [
"MIT"
] | 69 | 811756520cbfd8dce4427c53203ac193f61a94d1 | https://github.com/dionhaefner/delve/tree/811756520cbfd8dce4427c53203ac193f61a94d1 |
SPHead | # 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 ... | iofthetiger/pkuad | SPHead | false | 6,902 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
down | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
Thi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | CM-BF/FeatureFlow | down | false | 13,437 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
FocalLoss | import torch
from torch.nn import functional as F
from torch import nn
import torch.backends.cudnn
class FocalLoss(nn.Module):
def __init__(self, gamma):
super().__init__()
self.gamma = gamma
def forward(self, input, target):
if not target.size() == input.size():
raise Va... | 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 ... | cxz/tgs-salt-identification-challenge | FocalLoss | false | 6,516 | [
"MIT"
] | 1 | 859f3d7f2d3184532c42c34444500eec3b03b1c8 | https://github.com/cxz/tgs-salt-identification-challenge/tree/859f3d7f2d3184532c42c34444500eec3b03b1c8 |
GraphAttention | import torch
import torch.nn as nn
class GraphAttention(nn.Module):
def __init__(self, d_q, d_v, alpha, dropout=0.1):
super(GraphAttention, self).__init__()
self.dropout = nn.Dropout(dropout)
self.attention = nn.Linear(d_q + d_v, 1)
self.leaky_relu = nn.LeakyReLU(alpha)
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
from torch._inductor.runtime.... | YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions | GraphAttention | false | 14,716 | [
"MIT"
] | 62 | 6e5ef241c64b5b30a6ff54ddad31e610013b8388 | https://github.com/YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions/tree/6e5ef241c64b5b30a6ff54ddad31e610013b8388 |
SmallDecoder5_16x | # 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.... | EndyWon/Texture-Reformer | SmallDecoder5_16x | false | 8,163 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
SCNLayer | # 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... | ggoh29/Simplicial-neural-network-benchmark | SCNLayer | false | 6,733 | [
"MIT"
] | 1 | 9a12bcd054251790d85e3971f5473dcffaa5664b | https://github.com/ggoh29/Simplicial-neural-network-benchmark/tree/9a12bcd054251790d85e3971f5473dcffaa5664b |
FocalDiceLoss | # 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
... | ivadomed-profile-analysis-project/ivadomed | FocalDiceLoss | false | 15,656 | [
"MIT"
] | 87 | 3b53e2cb2b210511943da439401e2471fd387876 | https://github.com/ivadomed-profile-analysis-project/ivadomed/tree/3b53e2cb2b210511943da439401e2471fd387876 |
stack_pool | import torch
import torch.nn as nn
class stack_pool(nn.Module):
def __init__(self):
super(stack_pool, self).__init__()
self.pool2 = nn.MaxPool2d(2, stride=2)
self.pool2s1 = nn.MaxPool2d(2, stride=1)
self.pool3s1 = nn.MaxPool2d(3, stride=1, padding=1)
self.padding = nn.Repl... | 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... | ahhaa/crowdcount-stackpool | stack_pool | false | 6,133 | [
"MIT"
] | 1 | b849b72e88d5e53a9f6b5dbc93014668aee43fb4 | https://github.com/ahhaa/crowdcount-stackpool/tree/b849b72e88d5e53a9f6b5dbc93014668aee43fb4 |
L1NormLoss | # 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
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Karenou/mmfashion | L1NormLoss | false | 9,463 | [
"Apache-2.0"
] | 0 | dfc334232d1700cde18d144f983dd5b0a7f9852a | https://github.com/Karenou/mmfashion/tree/dfc334232d1700cde18d144f983dd5b0a7f9852a |
AuxiliaryConvolutions | import torch
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
class AuxiliaryConvolutions(nn.Module):
"""
Additional convolutions to produce higher-level feature maps.
"""
def __init__(self):
super(Auxilia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from ite... | mosevg/ssd | AuxiliaryConvolutions | false | 10,723 | [
"MIT"
] | 0 | 8fd9f6cc376c027427531bcf475188ae43c4b2d6 | https://github.com/mosevg/ssd/tree/8fd9f6cc376c027427531bcf475188ae43c4b2d6 |
BCEFocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch._utils
as... | ilcessadecalcular/segmentation | BCEFocalLoss | false | 10,583 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
SimpleAbsModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAbsModule(torch.nn.Module):
def __init__(self):
super(SimpleAbsModule, self).__init__()
def forward(self, a):
return torch.abs(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | briancoutinho/glow | SimpleAbsModule | false | 12,569 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
UpsamplingPixelShuffle | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import *
import torch.onnx
class shuffle(nn.Module):
def __init__(self, ratio):
super(shuffle, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 torchvision import models as models
import torch.nn.pa... | aalborov/openvino_training_extensions | UpsamplingPixelShuffle | false | 6,046 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
CapOnlyContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.init
def cosine_sim(im, s):
"""Cosine similarity between all the image and sentence pairs
"""
return im.mm(s.t())
def order_sim(im, s):
"""Order embeddings similarity measure $max(0, s-im)$
"""
YmX = s.unsqueeze(1).expand(s.size(0), im.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
import torch.nn as nn
import ... | ExplorerFreda/VSE-C | CapOnlyContrastiveLoss | false | 13,664 | [
"MIT"
] | 61 | 52d7742adfe017eacd74f36a5953ea2ace9f5fce | https://github.com/ExplorerFreda/VSE-C/tree/52d7742adfe017eacd74f36a5953ea2ace9f5fce |
MyNeural | import torch
import torch.nn
import torch.nn.functional as Functional
class MyNeural(torch.nn.Module):
def __init__(self, columns):
super(MyNeural, self).__init__()
self.f1 = torch.nn.Linear(columns, 32)
self.f2 = torch.nn.Linear(32, 16)
self.f3 = torch.nn.Linear(16, 1)
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
assert_size_s... | med-boubekri/Covid-Fact-Checker | MyNeural | false | 12,774 | [
"MIT"
] | 0 | 7869bcd830f33aefe4afeb5b75808f479e8094f2 | https://github.com/med-boubekri/Covid-Fact-Checker/tree/7869bcd830f33aefe4afeb5b75808f479e8094f2 |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | TuBui/deep_image_comparator | ContrastiveLoss | false | 1,153 | [
"MIT"
] | 0 | 2dea7738d794b91a960ee9f41461a4e3ffcd5e44 | https://github.com/TuBui/deep_image_comparator/tree/2dea7738d794b91a960ee9f41461a4e3ffcd5e44 |
SoftmaxCELoss | from torch.nn import Module
import torch
from torch.nn import functional as F
from torch import nn
class SoftmaxCELoss(Module):
def __init__(self, num_classes, num_features, dropout=0.5):
super(SoftmaxCELoss, self).__init__()
self.num_classes = num_classes
self.num_features = num_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Pandinosaurus/RandPerson | SoftmaxCELoss | false | 14,159 | [
"Apache-2.0"
] | 83 | 7dd503cc1d063d95b8cf6b43d40bb93452192d6d | https://github.com/Pandinosaurus/RandPerson/tree/7dd503cc1d063d95b8cf6b43d40bb93452192d6d |
ProteinResNetPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ProteinResNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_weights = nn.Linear(config.hidden_size, 1)
self.dense = nn.Linear(config.hidden_size, config.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, math as tl_math
im... | StephanHeijl/tape | ProteinResNetPooler | false | 4,747 | [
"BSD-3-Clause"
] | 0 | ec631ca53217686605477cf31af4fb8846ff660f | https://github.com/StephanHeijl/tape/tree/ec631ca53217686605477cf31af4fb8846ff660f |
LabelwiseAttention | # 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.... | JamesLYC88/LibMultiLabel | LabelwiseAttention | false | 9,161 | [
"MIT"
] | 0 | 042b76b3564409d916cf735ace617319009ae118 | https://github.com/JamesLYC88/LibMultiLabel/tree/042b76b3564409d916cf735ace617319009ae118 |
MaxPool2dDynamicSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
def __init__(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Exdenta/torchsat | MaxPool2dDynamicSamePadding | false | 13,659 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
WeightNet_DW | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightNet_DW(nn.Module):
""" Here we show a grouping manner when we apply
WeightNet to a depthwise convolution. The grouped
fc layer directly generates the convolutional kernel,
has fewer parameters while achieving comparable res... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Sense-GVT/BigPretrain | WeightNet_DW | false | 17,938 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
WayPoly | # 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... | klaudiapalasz/torchlayers | WayPoly | false | 15,835 | [
"MIT"
] | 573 | e6edd8797875325b7c0539d75a12f0d51f494127 | https://github.com/klaudiapalasz/torchlayers/tree/e6edd8797875325b7c0539d75a12f0d51f494127 |
convBlock | # 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_... | AlbertZhangHIT/DnCNN | convBlock | false | 18,420 | [
"MIT"
] | 2 | 8530dfa6d30424a04ae32ab036fd8cc4ac12e978 | https://github.com/AlbertZhangHIT/DnCNN/tree/8530dfa6d30424a04ae32ab036fd8cc4ac12e978 |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Encoder, self).__init__()
self.lin_input_to_hidden = nn.Linear(input_dim, hidden_dim)
self.lin_hidden_to_hidden = nn.Linear(hidden_dim, hidden_dim)
self.lin_hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abacoelho/variational-poisson-rnn | Encoder | false | 18,204 | [
"MIT"
] | 5 | abf77f79fc64be75ae9102ec8d537f77ed9c5f8f | https://github.com/abacoelho/variational-poisson-rnn/tree/abf77f79fc64be75ae9102ec8d537f77ed9c5f8f |
CDCM | # 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_... | arkel23/mmgeneration | CDCM | false | 9,943 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
KLDivergence | import torch
import torch.nn as nn
import torch.nn.functional as F
class KLDivergence(nn.Module):
"""A measure of how one probability distribution Q is different from a
second, reference probability distribution P.
Args:
tau (float): Temperature coefficient. Defaults to 1.0.
reduction (st... | 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... | ChenDirk/mmrazor | KLDivergence | false | 2,088 | [
"Apache-2.0"
] | 0 | 6f262ecd777c15efd4ee2d191cdc567071615421 | https://github.com/ChenDirk/mmrazor/tree/6f262ecd777c15efd4ee2d191cdc567071615421 |
MockOpticalFlowModel | import torch
import torch.nn as nn
class MockOpticalFlowModel(nn.Module):
def __init__(self, img_channels):
super().__init__()
self.model = nn.Conv2d(img_channels * 2, 2, kernel_size=1)
def forward(self, img1, img2):
x = torch.cat([img1, img2], dim=-3)
return self.model(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | neu-vig/ezflow | MockOpticalFlowModel | false | 16,159 | [
"MIT"
] | 94 | 1eb6f675e72b1de6db7b35d61ca4ef0082bae890 | https://github.com/neu-vig/ezflow/tree/1eb6f675e72b1de6db7b35d61ca4ef0082bae890 |
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