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 |
|---|---|---|---|---|---|---|---|---|---|---|
CustomizedNet | # 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 ... | EvelynQiang/analytics-zoo | CustomizedNet | false | 11,408 | [
"Apache-2.0"
] | 0 | be5dd08abe9b14ac085817decd017862a273985a | https://github.com/EvelynQiang/analytics-zoo/tree/be5dd08abe9b14ac085817decd017862a273985a |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-05)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | aris-mukherjee/TransUNet-modified | StdConv2d | false | 6,228 | [
"Apache-2.0"
] | 1 | 185307b677fd6ee05604213c90e14e028fab476a | https://github.com/aris-mukherjee/TransUNet-modified/tree/185307b677fd6ee05604213c90e14e028fab476a |
ActorNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class ActorNetwork(nn.Module):
def __init__(self, state_dim, action_dim, seed, fc1_units=256,
fc2_units=128):
""" Initialize parameters of model and build its.
Parameters:
===========
state_dim (int): State... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HatemSelim94/RL-MADDPG | ActorNetwork | false | 2,339 | [
"MIT"
] | 0 | 037a722f59e2e461fe6615685b434365fc5540b1 | https://github.com/HatemSelim94/RL-MADDPG/tree/037a722f59e2e461fe6615685b434365fc5540b1 |
NSELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | danielsuo/toy_flood | NSELoss | false | 15,113 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
AMCLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AMCLoss(nn.Module):
def __init__(self, in_features, out_features, s=None, m=None, device='cuda'
):
"""
Angular Margin Contrastive Loss
https://arxiv.org/pdf/2004.09805.pdf
Code converted ove... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GatorSense/LACE | AMCLoss | false | 5,198 | [
"MIT"
] | 1 | ee8194bc443886642f22c2317f5bdef23bba5147 | https://github.com/GatorSense/LACE/tree/ee8194bc443886642f22c2317f5bdef23bba5147 |
Mod | import torch
class Mod(torch.nn.Module):
def __init__(self):
super(Mod, self).__init__()
def forward(self, x, y):
return x % y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | PogChamper/torch2trt | Mod | false | 14,187 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DavidCarlyn/cnn_visualize | CNN | false | 2,158 | [
"MIT"
] | 0 | 6b4e554e1a6ac3b4951f0e914e0414cfa8bd3686 | https://github.com/DavidCarlyn/cnn_visualize/tree/6b4e554e1a6ac3b4951f0e914e0414cfa8bd3686 |
MetaLayerNorm | # 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 re
import warnings
import torch.nn as nn
from collections import Ordered... | Steffen-Wolf/pytorch-meta | MetaLayerNorm | false | 9,567 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ethan07902050/s3prl | SelfAttentionPooling | false | 2,277 | [
"MIT"
] | 0 | 854aff0b3062fc2cff531401923b8745f64701e7 | https://github.com/Ethan07902050/s3prl/tree/854aff0b3062fc2cff531401923b8745f64701e7 |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
def __init__(self, image=False):
super().__init__()
self.image = image
def forward(self, x, y):
x = x.sigmoid()
i, u = [(t.flatten(1).sum(1) if self.image else t.sum()) for t in [
x * y, x + y]]
... | 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... | agrawalshubham01/FracNet | DiceLoss | false | 9,727 | [
"Apache-2.0"
] | 0 | 8b912ca65651ff0ee203d9d73cf6ca18539728ac | https://github.com/agrawalshubham01/FracNet/tree/8b912ca65651ff0ee203d9d73cf6ca18539728ac |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
self.b = nn.Parame... | 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_... | HamoolNizar/RumorDetectionSystem | LayerNorm | false | 11,534 | [
"MIT"
] | 0 | 902ae4d705c0a6db470064f0e7f07f3c167d3eac | https://github.com/HamoolNizar/RumorDetectionSystem/tree/902ae4d705c0a6db470064f0e7f07f3c167d3eac |
SelfAttention | # 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... | daia99/brain-tokyo-workshop | SelfAttention | false | 15,111 | [
"Apache-2.0"
] | 1,097 | cd470255230afddba2b80d99a9641b682f4d0762 | https://github.com/daia99/brain-tokyo-workshop/tree/cd470255230afddba2b80d99a9641b682f4d0762 |
CumMax | # 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
... | Hritikbansal/RNNs_SVA_OOD | CumMax | false | 17,388 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 |
KLLoss | # 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
... | anurag1paul/pseudo_lidar | KLLoss | false | 6,224 | [
"MIT"
] | 1 | 02faf327efd43c986629d0ea797b058e464c05aa | https://github.com/anurag1paul/pseudo_lidar/tree/02faf327efd43c986629d0ea797b058e464c05aa |
ConvNet64 | import torch
import torch.nn as nn
def get_activation(s_act):
if s_act == 'relu':
return nn.ReLU(inplace=True)
elif s_act == 'sigmoid':
return nn.Sigmoid()
elif s_act == 'softplus':
return nn.Softplus()
elif s_act == 'linear':
return None
elif s_act == 'tanh':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Neural-Diffusion-Research/normalized-autoencoders | ConvNet64 | false | 8,646 | [
"MIT"
] | 30 | 0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 | https://github.com/Neural-Diffusion-Research/normalized-autoencoders/tree/0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 |
BertAttention | # 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.... | HS-YN/PanoAVQA | BertAttention | false | 18,384 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
GlobalAveragePooling | # 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... | CVPR2020/EnAET | GlobalAveragePooling | false | 17,082 | [
"MIT"
] | 3 | f490777980d20c68ca63764b7fc25537d7e72660 | https://github.com/CVPR2020/EnAET/tree/f490777980d20c68ca63764b7fc25537d7e72660 |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
import torch.utils.data
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
ret... | 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.utils.data
assert_size_stride = torch._C._dynamo.guard... | RichardDominik/AIC21-MTMC | AdaptiveAvgMaxPool2d | false | 14,320 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
gen_ab_cf | # 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.... | layel2/layyer-lib | gen_ab_cf | false | 3,884 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
ActorCriticModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorCriticModel(nn.Module):
def __init__(self, n_state, n_actions):
super(ActorCriticModel, self).__init__()
self.fc1 = nn.Linear(n_state, 16)
self.action1 = nn.Linear(16, 16)
self.action2 = nn.Linear(16, n_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nikolim/cablab | ActorCriticModel | false | 10,588 | [
"MIT"
] | 0 | 1dcf0d7da01ed3988f84309acfb31cc9a9893de1 | https://github.com/nikolim/cablab/tree/1dcf0d7da01ed3988f84309acfb31cc9a9893de1 |
PositionalEmbedding | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | WangDaYeeeeee/BERT-With-KnowledgeBase | PositionalEmbedding | false | 2,955 | [
"Apache-2.0"
] | 0 | 5f205295ce9b69ab0f813ef34409fdf2de3a14ca | https://github.com/WangDaYeeeeee/BERT-With-KnowledgeBase/tree/5f205295ce9b69ab0f813ef34409fdf2de3a14ca |
WeightedMCEloss | import torch
import torch.nn as nn
import torch.nn.functional as F
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class WeightedMCEloss(nn.Module):
... | 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
... | CarlosPena00/pytorch-unet | WeightedMCEloss | false | 210 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
AsymmetricLossOptimized | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | LanXiangExcavator/python-classifier-2021 | AsymmetricLossOptimized | false | 11,621 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b |
PredictionConvolutions | import torch
from torch import nn
from itertools import product as product
import torch.optim
import torch.utils.data
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 itertools import product as product
import torch.optim... | mosevg/ssd | PredictionConvolutions | false | 10,793 | [
"MIT"
] | 0 | 8fd9f6cc376c027427531bcf475188ae43c4b2d6 | https://github.com/mosevg/ssd/tree/8fd9f6cc376c027427531bcf475188ae43c4b2d6 |
NoiseNet | import torch
import torch.nn.functional as F
from torch import nn
class NoiseNet(nn.Module):
def __init__(self, channels=3, kernel_size=5):
super(NoiseNet, self).__init__()
self.kernel_size = kernel_size
self.channels = channels
to_pad = int((self.kernel_size - 1) / 2)
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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GuYuanjie/Deep-Retinex-fusion | NoiseNet | false | 17,351 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
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 numpy as np
import tor... | BruceChanJianLe/drlnd-tennis-project3 | Critic | false | 11,266 | [
"MIT"
] | 0 | cb2b880c55eedb6eef3775ed19e90aeec60174d8 | https://github.com/BruceChanJianLe/drlnd-tennis-project3/tree/cb2b880c55eedb6eef3775ed19e90aeec60174d8 |
CrossEntropyLossSoft | # 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.... | Ren-Research/maestro | CrossEntropyLossSoft | false | 2,919 | [
"MIT"
] | 0 | b89e171d51ec910b165b9b01dd8373848a6207f7 | https://github.com/Ren-Research/maestro/tree/b89e171d51ec910b165b9b01dd8373848a6207f7 |
SegmentalTransformerEncoder | import torch
import numpy as np
from torch import Tensor
import torch.nn as nn
from torch.nn.modules.transformer import TransformerEncoderLayer
from torch.nn.modules.transformer import _get_clones
class InitialSpanEncoder(TransformerEncoderLayer):
"""
The initial layer for the Segmental Transformer Encoder. R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cmdowney88/XLSLM | SegmentalTransformerEncoder | false | 3,302 | [
"MIT"
] | 0 | 7fe266bd0f0ad8a79a30052a18104b974d1c32e8 | https://github.com/cmdowney88/XLSLM/tree/7fe266bd0f0ad8a79a30052a18104b974d1c32e8 |
EuclideanDistance | import torch
from torch import Tensor
import torch.utils.data.dataloader
from torch import nn
import torch.nn
def arccosh(x):
"""Compute the arcosh, numerically stable."""
x = torch.clamp(x, min=1 + EPSILON)
a = torch.log(x)
b = torch.log1p(torch.sqrt(x * x - 1) / x)
return a + b
def mdot(x, y):... | 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.dataloader
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | ParikhKadam/flair | EuclideanDistance | false | 14,164 | [
"MIT"
] | 7,539 | a1732bc5ab0b4aeb09d1ed3a630ae2fd8fa095ef | https://github.com/ParikhKadam/flair/tree/a1732bc5ab0b4aeb09d1ed3a630ae2fd8fa095ef |
CE | import torch
import torch.nn as nn
class CE(nn.Module):
def __init__(self):
super(CE, self).__init__()
def forward(self, mat1, mat2):
return -torch.mean(mat2 * torch.log(mat1 + 1e-10) + (1 - mat2) *
torch.log(1 - mat1 + 1e-10))
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Jiangtong-Li/ZHSIR | CE | false | 17,493 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
Squash | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
... | mcx/annotated_deep_learning_paper_implementations | Squash | false | 7,207 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
BiasAdd | # 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
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
assert_size_str... | Desmond-97/RepVGG | BiasAdd | false | 9,015 | [
"MIT"
] | 0 | 147490c54ee7b83d4a432a5913b17c8800e55d06 | https://github.com/Desmond-97/RepVGG/tree/147490c54ee7b83d4a432a5913b17c8800e55d06 |
DCCWeightedELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | lbasora/DCC | DCCWeightedELoss | false | 12,741 | [
"MIT"
] | 0 | c9abcd7d697cc9e50e874286f1edfb3be93ce6d9 | https://github.com/lbasora/DCC/tree/c9abcd7d697cc9e50e874286f1edfb3be93ce6d9 |
ReverseMaskConv | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Vious/LBAM_Pytorch | ReverseMaskConv | false | 14,581 | [
"MIT"
] | 112 | b9292440e7a7559c027f48d6fd061dcabc41a6bf | https://github.com/Vious/LBAM_Pytorch/tree/b9292440e7a7559c027f48d6fd061dcabc41a6bf |
TransposeAdaINConv2dLayer | # 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 ... | LenKerr/Semantic-Colorization-GAN | TransposeAdaINConv2dLayer | false | 5,529 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | manjuransari/petastorm | Net | false | 16,001 | [
"Apache-2.0"
] | 1,393 | 1af7212a1293b1edb78767a359aa2b60db24b71b | https://github.com/manjuransari/petastorm/tree/1af7212a1293b1edb78767a359aa2b60db24b71b |
SCLN | # 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 ... | dtx525942103/Cross-Speaker-Emotion-Transfer | SCLN | false | 10,060 | [
"MIT"
] | 0 | 195c3bf227f4de98942e17327ff26e728366022b | https://github.com/dtx525942103/Cross-Speaker-Emotion-Transfer/tree/195c3bf227f4de98942e17327ff26e728366022b |
TransformerEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different represen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | verages/PaddleOCR2Pytorch | TransformerEncoderLayer | false | 4,690 | [
"Apache-2.0"
] | 0 | 201f0d5d6007f49620c49af7d222c3b220eb3e70 | https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization
(https://arxiv.org/abs/1607.06450)
"""
def __init__(self, normalized_shape, eps=1e-05):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(normalized_shape))
self.bet... | 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_... | srlee-ai/claf | LayerNorm | false | 10,885 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 |
MLP_HD | # 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.... | NaiboWang/HFL-CS6203-NaiboShiqi | MLP_HD | false | 9,361 | [
"MIT"
] | 0 | 4bab35a20f1ec1229b0011c952d93c341579c402 | https://github.com/NaiboWang/HFL-CS6203-NaiboShiqi/tree/4bab35a20f1ec1229b0011c952d93c341579c402 |
Classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.fc1 = nn.Linear(900, 3)
def forward(self, x):
x = F.avg_pool2d(x, 8)
x = x.view(-1, 900)
x = self.fc1(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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | helinwang/pytorch-semseg | Classifier | false | 6,794 | [
"MIT"
] | 1 | 117e5fb8afbad87d6968de1683867854ddec5885 | https://github.com/helinwang/pytorch-semseg/tree/117e5fb8afbad87d6968de1683867854ddec5885 |
PolicyNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class PolicyNet(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNet, self).__init__()
self.log_std_min = 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 import triton_helpers
import torch.nn as nn
from to... | JohnJim0816/rl-tutorials | PolicyNet | false | 8,370 | [
"MIT"
] | 16 | e99daea815da85f9f25dff2d01b030249a203d22 | https://github.com/JohnJim0816/rl-tutorials/tree/e99daea815da85f9f25dff2d01b030249a203d22 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GavinGuan95/Generative-VQA | ScaledDotProductAttention | false | 5,225 | [
"MIT"
] | 1 | 0912e3a2426809ef4d4eb40bae667b31c2269161 | https://github.com/GavinGuan95/Generative-VQA/tree/0912e3a2426809ef4d4eb40bae667b31c2269161 |
CoordConv | # 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 torch.nn.utils import spectral_norm
assert_size_strid... | HexagonPrime/pixel-nerf | CoordConv | false | 2,416 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
TensorMean | import torch
class StatModule(torch.nn.Module):
def __init__(self, dim, keepdim=False):
if isinstance(dim, list):
dim = tuple(dim)
if isinstance(dim, int):
dim = dim,
assert isinstance(dim, tuple)
self.dim = dim
self.keepdim = keepdim
super(... | 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... | Minyus/pipelinex | TensorMean | false | 14,055 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
InfoLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
assert_size_stride = torch._... | Joshua-Schroijen/deepproblog | InfoLoss | false | 670 | [
"Apache-2.0"
] | 0 | 4ae56f1e860010b7857b29d5bd76fb1555d5e19d | https://github.com/Joshua-Schroijen/deepproblog/tree/4ae56f1e860010b7857b29d5bd76fb1555d5e19d |
statm_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... | COMP6248-Reproducability-Challenge/KD_SRRL | statm_loss | false | 7,817 | [
"MIT"
] | 27 | 958c8f9fbeb7893f9bd866aff5b065b2bde87f23 | https://github.com/COMP6248-Reproducability-Challenge/KD_SRRL/tree/958c8f9fbeb7893f9bd866aff5b065b2bde87f23 |
WeightedSumLoss | # 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
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | aalborov/openvino_training_extensions | WeightedSumLoss | false | 6,044 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
Whitening2d | import torch
import torch.nn as nn
from torch.cuda.amp import custom_fwd
from torch.nn.functional import conv2d
class Whitening2d(nn.Module):
def __init__(self, output_dim: 'int', eps: 'float'=0.0):
"""Layer that computes hard whitening for W-MSE using the Cholesky decomposition.
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | xwyzsn/solo-learn | Whitening2d | false | 16,750 | [
"MIT"
] | 693 | 16d021d8053439a3de205337ab2a11d191500b09 | https://github.com/xwyzsn/solo-learn/tree/16d021d8053439a3de205337ab2a11d191500b09 |
InvHuberLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InvHuberLoss(nn.Module):
"""Inverse Huber Loss for depth estimation.
The setup is taken from https://arxiv.org/abs/1606.00373
Args:
ignore_index (float): value to ignore in the target
when computin... | 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
... | DrSleep/DenseTorch | InvHuberLoss | false | 13,590 | [
"MIT"
] | 69 | f90bef075429d763fc08338dea8222d28b0a4516 | https://github.com/DrSleep/DenseTorch/tree/f90bef075429d763fc08338dea8222d28b0a4516 |
LinearAverage | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
as... | VisionLearningGroup/CDS | LinearAverage | false | 18,045 | [
"MIT"
] | 7 | 5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc | https://github.com/VisionLearningGroup/CDS/tree/5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
"""Horizontally flip a tensor image or a batch of tensor images.
.. image:: _static/img/hflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tens... | 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... | bkntr/kornia | Hflip | false | 3,217 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | aa31f8d730864c71948cef32f9d3ed9138401755 | https://github.com/bkntr/kornia/tree/aa31f8d730864c71948cef32f9d3ed9138401755 |
GroupNorm | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class GroupNorm(Module):
"""
## Group Normalization Layer
"""
def __init__(self, groups: 'int', channels: 'int', *, eps: float=1e-05,
affine: bool=True):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch import nn
import torch.utils.data
import... | Aarsh2001/annotated_deep_learning_paper_implementations | GroupNorm | false | 4,780 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
GlobalAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class GlobalAttention(nn.Module):
"""
Global attention takes a matrix and a query vector. It
then computes a parameterized convex combination of the matrix
based on the input query.
Constructs a unit mapping a quer... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Roc-Ng/HANet | GlobalAttention | false | 8,702 | [
"MIT"
] | 34 | e679703e9e725205424d87f750358fb4f62ceec5 | https://github.com/Roc-Ng/HANet/tree/e679703e9e725205424d87f750358fb4f62ceec5 |
Quantization | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | skipper17/Invertible-Image-Rescaling | Quantization | false | 12,989 | [
"Apache-2.0"
] | 0 | 4755f21faa5f7c4599dfb971a875ecee86bc35a1 | https://github.com/skipper17/Invertible-Image-Rescaling/tree/4755f21faa5f7c4599dfb971a875ecee86bc35a1 |
L1_log | # 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
... | Khoronus/MonoDepth-FPN-PyTorch | L1_log | false | 714 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
MultiHeadAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | UoMfzp/transformer-xl-Chinese-Pytorch | MultiHeadAttn | false | 11,959 | [
"Apache-2.0"
] | 0 | 435641ed138e81f949c5b557b5a13c0a09fb6018 | https://github.com/UoMfzp/transformer-xl-Chinese-Pytorch/tree/435641ed138e81f949c5b557b5a13c0a09fb6018 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SarodYatawatta/federated-pytorch-test | Net | false | 8,764 | [
"Apache-2.0"
] | 33 | 42a51ba12a92b32fa19273340d5b61e74e11d8e0 | https://github.com/SarodYatawatta/federated-pytorch-test/tree/42a51ba12a92b32fa19273340d5b61e74e11d8e0 |
Embedding | # 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 ... | CFM-MSG/Code_TFUN | Embedding | false | 2,076 | [
"MIT"
] | 0 | 39aebd748a0191e532eb81144386741e98a58e73 | https://github.com/CFM-MSG/Code_TFUN/tree/39aebd748a0191e532eb81144386741e98a58e73 |
ModulatedConv2d | # 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 ... | Theomat/colorization-av-enseirb-2020 | ModulatedConv2d | false | 14,474 | [
"Apache-2.0"
] | 1,422 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | https://github.com/Theomat/colorization-av-enseirb-2020/tree/c54c2388ea39a62289fa2f1c51b4757bf55d3c4f |
UNet | # 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 ... | arpan-dhatt/oidn | UNet | false | 14,956 | [
"Apache-2.0"
] | 1,206 | 9419411ba4b343b475b53587cadd44c83d68dc2a | https://github.com/arpan-dhatt/oidn/tree/9419411ba4b343b475b53587cadd44c83d68dc2a |
ScModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | almaan/STereoSCope | ScModel | false | 1,417 | [
"MIT"
] | 0 | 8f6a2021b6cb73aecda14f6bbbd25e26bfc9301a | https://github.com/almaan/STereoSCope/tree/8f6a2021b6cb73aecda14f6bbbd25e26bfc9301a |
GraphConvolution | # 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.nn import Module
from torch import nn
import torch.autograd
from torc... | sumanmichael/Palmira_pb | GraphConvolution | false | 4,397 | [
"MIT"
] | 0 | 8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 | https://github.com/sumanmichael/Palmira_pb/tree/8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlphaLFC/mmdetection | BalancedL1Loss | false | 4,839 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
TransposedConvModel | import torch
import torch.cuda
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
class TransposedConvModel(torch.nn.Module):
def __init__(self):
super(TransposedConvModel, self).__init__()
self.conv1 = torch.nn.ConvTranspose2d(10, 10, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.cuda
import torc... | mikeseven/aimet | TransposedConvModel | false | 11,116 | [
"BSD-3-Clause"
] | 0 | 63211a4f259b6457c58dfae1097c70acb93319fe | https://github.com/mikeseven/aimet/tree/63211a4f259b6457c58dfae1097c70acb93319fe |
MaxMarginCriterion | import torch
import torch.nn as nn
class MaxMarginCriterion(nn.Module):
def __init__(self, visual_rank_weight, lang_rank_weight, margin):
super(MaxMarginCriterion, self).__init__()
self.visual_rank = visual_rank_weight > 0
self.lang_rank = lang_rank_weight > 0
self.visual_rank_wei... | 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... | kmario23/MAttNet | MaxMarginCriterion | false | 7,047 | [
"MIT"
] | 1 | 0d66321eb5dc9c8523a5ebf45f608b0672b051ab | https://github.com/kmario23/MAttNet/tree/0d66321eb5dc9c8523a5ebf45f608b0672b051ab |
STFullyConnected | # 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.... | naisuu/DrugEx | STFullyConnected | false | 4,074 | [
"MIT"
] | 0 | 8708c98a137473f11990d70e43a46018806b6f39 | https://github.com/naisuu/DrugEx/tree/8708c98a137473f11990d70e43a46018806b6f39 |
ClassificationModel | # 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_... | abhi1kumar/AP-loss | ClassificationModel | false | 14,750 | [
"MIT"
] | 158 | 87f51b212761ef233422dbaaf799444fb453a10e | https://github.com/abhi1kumar/AP-loss/tree/87f51b212761ef233422dbaaf799444fb453a10e |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... | 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... | Sandy1230/Dive-into-DL-PyTorch-master | GlobalAvgPool2d | false | 17,880 | [
"Apache-2.0"
] | 4 | eca149f6b706a4e6a7b377707deab22341b014d1 | https://github.com/Sandy1230/Dive-into-DL-PyTorch-master/tree/eca149f6b706a4e6a7b377707deab22341b014d1 |
CE | # 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
... | Jiangtong-Li/ZHSIR | CE | false | 17,493 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
FocalLoss | import torch
import torch.nn as nn
from typing import Optional
def focal_loss(input: 'torch.Tensor', target: 'torch.Tensor', gamma:
'float'=0, weight: 'Optional[torch.Tensor]'=None) ->torch.Tensor:
"""
Returns the focal loss between `target` and `input`
:math:`\\text{FL}(p_t)=-(1-p_t)^\\gamma\\log(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 math as tl_math
import torch.nn as nn
... | Vermeille/Torchelie | FocalLoss | false | 14,550 | [
"MIT"
] | 117 | 43957d83238372ae6436aac90127865c2040b76c | https://github.com/Vermeille/Torchelie/tree/43957d83238372ae6436aac90127865c2040b76c |
AttDec | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
if m.bias is not None:
m.bias.data.fill_(0)
elif classname.find('BatchNorm'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | e96031413/tfvaegan | AttDec | false | 10,116 | [
"MIT"
] | 0 | 4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 | https://github.com/e96031413/tfvaegan/tree/4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 |
MatchRNNAttention | import torch
import torch.utils.data
import torch.nn.functional as F
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | xdong73S/Match_LSTM_v2.0 | MatchRNNAttention | false | 4,575 | [
"MIT"
] | 0 | dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 | https://github.com/xdong73S/Match_LSTM_v2.0/tree/dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise.
Code from https://github.com/adambielski/siamese-triplet"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | anish-lu-yihe/abcpy | ContrastiveLoss | false | 6,208 | [
"BSD-3-Clause-Clear"
] | 1 | be58367c4d7e38ee696238e3d8405e8abe2defb7 | https://github.com/anish-lu-yihe/abcpy/tree/be58367c4d7e38ee696238e3d8405e8abe2defb7 |
Pow | # 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... | yifanpu001/PytorchToCaffe | Pow | false | 4,706 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
GlobalAvgPool2d | # 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... | CoDaS-Lab/Contextual-Adversarial-Patches | GlobalAvgPool2d | false | 2,100 | [
"MIT"
] | 0 | ffbd897174fc381ba7c3ba1e6f827b84ccb30fd4 | https://github.com/CoDaS-Lab/Contextual-Adversarial-Patches/tree/ffbd897174fc381ba7c3ba1e6f827b84ccb30fd4 |
_ASPPModule | # 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... | cplusx/SIGN | _ASPPModule | false | 1,739 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae |
avgpool | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class avgpool(nn.Module):
"""
Mean pooling class - downsampling
"""
def __init__(self, up_size=0):
super(avgpool, self).__init__()
def forward(self, x):
out_man = (x[:, :, ::2, ::2] + x[:, :, 1::2... | 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | nathalia-kim/nu_gan | avgpool | false | 10,714 | [
"MIT"
] | 0 | c1d0891945bd7ac3d95869db91f490f57f203110 | https://github.com/nathalia-kim/nu_gan/tree/c1d0891945bd7ac3d95869db91f490f57f203110 |
ParityPonderGRU | # 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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import... | techthiyanes/annotated_deep_learning_paper_implementations | ParityPonderGRU | false | 16,605 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
BitEstimator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Bitparm(nn.Module):
"""
save params
"""
def __init__(self, channel, final=False):
super(Bitparm, self).__init__()
self.final = final
self.h = nn.Parameter(torch.nn.init.normal_(tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
import t... | Geunwoo-Jeon/iclr_17_compression | BitEstimator | false | 13,725 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
NextSentencePrediction | # 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.... | EddieMG/LateTemporalModeling3DCNN | NextSentencePrediction | false | 2,284 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
ContextPooler | # 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... | c370300679/ClinicalTransformerNER | ContextPooler | false | 12,189 | [
"MIT"
] | 0 | 4a4a796775f75f6d5adc053e956ec6a0ae6fe2f3 | https://github.com/c370300679/ClinicalTransformerNER/tree/4a4a796775f75f6d5adc053e956ec6a0ae6fe2f3 |
Posterior | import torch
import torch.nn as nn
class Posterior(nn.Module):
def __init__(self, z_dim, hidden_dim, obs_dim):
super(Posterior, self).__init__()
self.z_obs_to_hidden = nn.Linear(2 * z_dim + obs_dim, hidden_dim)
self.hidden_to_hidden = nn.Linear(hidden_dim, hidden_dim)
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | morimo27182/DeepKalmanFilter | Posterior | false | 12,799 | [
"MIT"
] | 0 | 5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 | https://github.com/morimo27182/DeepKalmanFilter/tree/5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 |
BinaryCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DerekRay/2020-instanceSeg | BinaryCrossEntropyLoss | false | 7,948 | [
"MIT"
] | 25 | a08ad95e64726db53cc32a5f90aaa13ae3cdb6a3 | https://github.com/DerekRay/2020-instanceSeg/tree/a08ad95e64726db53cc32a5f90aaa13ae3cdb6a3 |
Decoder | # 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
import torch.... | hzxie/RMNet | Decoder | false | 16,049 | [
"MIT"
] | 66 | 32a16f9c9473463a41dd6e95f72b06dd830fc1eb | https://github.com/hzxie/RMNet/tree/32a16f9c9473463a41dd6e95f72b06dd830fc1eb |
simple_decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils
import torch.distributions
assert_size_s... | Butters-cloud/denoising-normalizing-flow | simple_decoder | false | 7,839 | [
"MIT"
] | 12 | 12d56a0d069e10a744acabf5e78fdbfba8df54ee | https://github.com/Butters-cloud/denoising-normalizing-flow/tree/12d56a0d069e10a744acabf5e78fdbfba8df54ee |
DuelingMLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class DuelingMLP(nn.Module):
def __init__(self, state_size, num_actions):
super().__init__()
self.linear = nn.Linear(state_size, 256)
self.value_head = nn.Linear(256, 1)
self.advantage_head = nn.Linear(256, num_act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AlexHermansson/hindsight-experience-replay | DuelingMLP | false | 16,876 | [
"MIT"
] | 5 | 65468d523bb803123d7aab9bb83abc7a3d5db3c8 | https://github.com/AlexHermansson/hindsight-experience-replay/tree/65468d523bb803123d7aab9bb83abc7a3d5db3c8 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Blair129/FEAT-master | ScaledDotProductAttention | false | 8,939 | [
"MIT"
] | 0 | 459e05000a8cca5421fafb7d2f33f19418378df7 | https://github.com/Blair129/FEAT-master/tree/459e05000a8cca5421fafb7d2f33f19418378df7 |
Contract | # 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | DDGRCF/YOLOX_OBB | Contract | false | 7,961 | [
"Apache-2.0"
] | 39 | 27b80953306492b8bc83b86b1353d8cee01ef9b6 | https://github.com/DDGRCF/YOLOX_OBB/tree/27b80953306492b8bc83b86b1353d8cee01ef9b6 |
Pointer | # 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
assert_size_stride = torch... | timgianitsos/squad | Pointer | false | 13,190 | [
"MIT"
] | 0 | 6ab502652e3528cfeeddfb8eba05221443a35294 | https://github.com/timgianitsos/squad/tree/6ab502652e3528cfeeddfb8eba05221443a35294 |
DecoderSlot | # 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... | CatarauCorina/representation_learning | DecoderSlot | false | 8,954 | [
"Apache-2.0"
] | 0 | bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 | https://github.com/CatarauCorina/representation_learning/tree/bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 |
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 ... | Jiang-HB/AC_CDQ | Critic | false | 18,375 | [
"MIT"
] | 7 | 4b4ec2d611c4481ad0b99cf7ea79eb23014a0325 | https://github.com/Jiang-HB/AC_CDQ/tree/4b4ec2d611c4481ad0b99cf7ea79eb23014a0325 |
Homography | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | lyhyl/kornia | Homography | false | 12,740 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 5bd3aeb0d54dedac01e6eaf8bac37779bab0bec5 | https://github.com/lyhyl/kornia/tree/5bd3aeb0d54dedac01e6eaf8bac37779bab0bec5 |
My_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | H-Liu1997/Pytorch_Pose_Estimation_Framework | My_loss | false | 5,250 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
L2Norm | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | Atine/pytorch.SSD.handles | L2Norm | false | 8,824 | [
"MIT"
] | 0 | ff57ceacc57f195361adceb92a84d54d155ba1a4 | https://github.com/Atine/pytorch.SSD.handles/tree/ff57ceacc57f195361adceb92a84d54d155ba1a4 |
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_... | HAXRD/PIC | LayerNorm | false | 8,177 | [
"MIT"
] | 28 | 658b4dd6b01e64413d5f8f0107d9167f1bd78546 | https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546 |
AttendNodeModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class AttendNodeModule(nn.Module):
def forward(self, node_vectors, query):
"""
Args:
node_vectors [Tensor] (num_node, dim_v) : node feature vectors
query [Tensor] (dim_v, ) : query v... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | qiuyue1993/XNM-Net | AttendNodeModule | false | 16,297 | [
"MIT"
] | 95 | 1c4a16fd745d9e90e0d7a08b21e7efca4d2c6195 | https://github.com/qiuyue1993/XNM-Net/tree/1c4a16fd745d9e90e0d7a08b21e7efca4d2c6195 |
TripletLoss | # 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.... | WangWenhao0716/DomainMix | TripletLoss | false | 18,059 | [
"MIT"
] | 8 | 2d9a20c1536177d1d71fbdc99f714eaf98fdfe92 | https://github.com/WangWenhao0716/DomainMix/tree/2d9a20c1536177d1d71fbdc99f714eaf98fdfe92 |
KL | import torch
import torch.nn as nn
import torch.nn.functional as F
class KL(nn.Module):
def __init__(self, reduction='batchmean'):
super(KL, self).__init__()
self.reduction = reduction
def forward(self, input, target):
input = input.float()
target = target.float()
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 libdevice, math as tl_math
import torc... | Raiselimit/TorchBlocks | KL | false | 5,741 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
GatingMechanism | # 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 ... | victor-psiori/Transformers-RL | GatingMechanism | false | 16,680 | [
"MIT"
] | 50 | 85b3f2376ba473a45ca18c969aebb1ae82cf8475 | https://github.com/victor-psiori/Transformers-RL/tree/85b3f2376ba473a45ca18c969aebb1ae82cf8475 |
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