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 |
|---|---|---|---|---|---|---|---|---|---|---|
AsymmetricLoss | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=True):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torchv... | MinliangLin/ASL | AsymmetricLoss | false | 2,921 | [
"MIT"
] | 0 | beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 | https://github.com/MinliangLin/ASL/tree/beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 |
RobertaMaskLeanerHead | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class RobertaMaskLeanerHead(nn.Module):
"""
Head for mask leaner.
input: (batch, src_lens, embed_dim)
output: (batch, src_lens，1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChanLiang/MAP-BERT | RobertaMaskLeanerHead | false | 275 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
EltwiseProdScoring | import torch
import torch.nn as nn
class EltwiseProdScoring(nn.Module):
"""
Linearly mapping h and v to the same dimension, and do a elementwise
multiplication and a linear scoring
"""
def __init__(self, h_dim, a_dim, dot_dim=256):
"""Initialize layer."""
super(EltwiseProdScoring,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cacosandon/speaker_follower_with_objects | EltwiseProdScoring | false | 12,194 | [
"BSD-2-Clause",
"MIT"
] | 0 | f3d454fdbd1c8129887cf4ecc4743d231c7b9555 | https://github.com/cacosandon/speaker_follower_with_objects/tree/f3d454fdbd1c8129887cf4ecc4743d231c7b9555 |
AdaptiveConcatPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.a... | MichoelSnow/data_science | AdaptiveConcatPool2d | false | 9,865 | [
"MIT"
] | 0 | 7f6c054624268308ec4126a601c9fa8bc5de157c | https://github.com/MichoelSnow/data_science/tree/7f6c054624268308ec4126a601c9fa8bc5de157c |
FunctionalRelu6 | # 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... | Ilyabasharov/torch2trt | FunctionalRelu6 | false | 2,511 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
Perceptron | # 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_... | negotiatorvivian/PDP-SP | Perceptron | false | 4,065 | [
"MIT"
] | 0 | 0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 | https://github.com/negotiatorvivian/PDP-SP/tree/0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 |
MiniBatchStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
Such kind of operation has been wi... | 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.distributed as dist
import torch.autograd as... | HXWAndCL/mmgeneration | MiniBatchStddevLayer | false | 5,253 | [
"Apache-2.0"
] | 1 | 9afb1d740bf56a4ecde5064d5bb2a4e2d777638b | https://github.com/HXWAndCL/mmgeneration/tree/9afb1d740bf56a4ecde5064d5bb2a4e2d777638b |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | hologerry/style-based-gan-pytorch | EqualConv2d | false | 3,623 | [
"MIT"
] | 0 | 1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 | https://github.com/hologerry/style-based-gan-pytorch/tree/1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 |
ActivationQuantizer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import F... | RiccardoRuggiero/micronet | ActivationQuantizer | false | 5,766 | [
"MIT"
] | 1 | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | https://github.com/RiccardoRuggiero/micronet/tree/bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 |
DiceLoss | import torch
import torch.nn as nn
def f_score(pr, gt, beta=1, eps=1e-07, threshold=None, activation='sigmoid'):
activation_fn = torch.nn.Sigmoid()
pr = activation_fn(pr)
if threshold is not None:
pr = (pr > threshold).float()
tp = torch.sum(gt * pr)
fp = torch.sum(pr) - tp
fn = torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LovreAB17/Eff-UNet | DiceLoss | false | 17,601 | [
"MIT"
] | 5 | b1e76a68d96e55324b6859c64ad2367653143e5e | https://github.com/LovreAB17/Eff-UNet/tree/b1e76a68d96e55324b6859c64ad2367653143e5e |
eSEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | UWO-CCPL/AdelaiDet | eSEModule | false | 9,565 | [
"BSD-2-Clause"
] | 0 | 29a59575697dbbb4cfe7b7ab821805913348cf61 | https://github.com/UWO-CCPL/AdelaiDet/tree/29a59575697dbbb4cfe7b7ab821805913348cf61 |
NoNorm | import torch
from torch import nn
import torch.utils.checkpoint
class NoNorm(nn.Module):
def __init__(self, feat_size, eps=None):
super().__init__()
self.bias = nn.Parameter(torch.zeros(feat_size))
self.weight = nn.Parameter(torch.ones(feat_size))
def forward(self, input_tensor):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Clemens123/transformers | NoNorm | false | 11,500 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
RED_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_... | BennyZhang-Codes/LDCT-denoising-with-DL-Methods-and-Dicom-Viewer-by-Benny | RED_CNN | false | 17,036 | [
"MIT"
] | 7 | 07e3dc1e3c6dcdea314b2a9e3cf9ac1036cf5eb6 | https://github.com/BennyZhang-Codes/LDCT-denoising-with-DL-Methods-and-Dicom-Viewer-by-Benny/tree/07e3dc1e3c6dcdea314b2a9e3cf9ac1036cf5eb6 |
TKipfGCN | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zxhhh97/cogdl | TKipfGCN | false | 4,682 | [
"MIT"
] | 0 | de21c78d9bbbf0c6cafbc72ff241cda35693ec37 | https://github.com/zxhhh97/cogdl/tree/de21c78d9bbbf0c6cafbc72ff241cda35693ec37 |
CausualConv | # 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... | shaun95/StarGANv2-VC | CausualConv | false | 16,394 | [
"MIT"
] | 116 | ed20538971a03d699351a349a3631767333baeb7 | https://github.com/shaun95/StarGANv2-VC/tree/ed20538971a03d699351a349a3631767333baeb7 |
AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Prince326/PySyft | AvgPool2d | false | 9,357 | [
"Apache-2.0"
] | 0 | c7167680e9020853c353a2a725ff79f3df2bef05 | https://github.com/Prince326/PySyft/tree/c7167680e9020853c353a2a725ff79f3df2bef05 |
down | # 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... | Remosy/v2e | down | false | 11,836 | [
"MIT"
] | 0 | efc81cbcc113ca55d1631603323150be5ef8eb30 | https://github.com/Remosy/v2e/tree/efc81cbcc113ca55d1631603323150be5ef8eb30 |
SelfAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfAttention(nn.Module):
def __init__(self, k, heads=8):
super().__init__()
self.k = k
self.heads = heads
self.to_keys = nn.Linear(k, k * heads, bias=False)
self.to_queries = nn.Linear(k, k * heads, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dogeplusplus/sandbox | SelfAttention | false | 1,864 | [
"MIT"
] | 0 | c9041c06da9454f6c3cec622abbbf918c9f13bdc | https://github.com/dogeplusplus/sandbox/tree/c9041c06da9454f6c3cec622abbbf918c9f13bdc |
qd | # 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_... | sautami26/DIVA | qd | false | 4,266 | [
"MIT"
] | 0 | 52af683db216cb6e2ac777597fd9ec744ce7c8f2 | https://github.com/sautami26/DIVA/tree/52af683db216cb6e2ac777597fd9ec744ce7c8f2 |
FiLM | import torch
import torch.nn as nn
class FiLM(nn.Module):
def __init__(self, zdim, maskdim):
super(FiLM, self).__init__()
self.gamma = nn.Linear(zdim, maskdim)
self.beta = nn.Linear(zdim, maskdim)
def forward(self, x, z):
gamma = self.gamma(z).unsqueeze(-1).unsqueeze(-1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CPJKU/audio_conditioned_unet | FiLM | false | 7,821 | [
"MIT"
] | 20 | 68f20f5280079e99be260f9fe9933c0064eb2d7f | https://github.com/CPJKU/audio_conditioned_unet/tree/68f20f5280079e99be260f9fe9933c0064eb2d7f |
GatedConv | import torch
import torch.nn as nn
class GatedConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, groups=1):
super(GatedConv, self).__init__()
self.layer_f = nn.Conv2d(in_channels, out_channels, kernel_size,
stride=stride, padding=p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | D-hash-code/ffjord-rnode-finalweek-mnist | GatedConv | false | 2,148 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
InceptionD | # 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 ... | Galaxies99/inception-cuda | InceptionD | false | 11,447 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
CasualConv1d | import torch
import torch.nn as nn
class CasualConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(CasualConv1d, self).__init__()
self.dilation = dilation
padding = dilation * (kernel_size - 1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Asichurter/Few-Shot-Project | CasualConv1d | false | 80 | [
"MIT"
] | 0 | 865cd6aa7b996c518dfa48dcc9ffad90445f9efe | https://github.com/Asichurter/Few-Shot-Project/tree/865cd6aa7b996c518dfa48dcc9ffad90445f9efe |
LinearWeightNorm | import torch
import torch.nn as nn
class LinearWeightNorm(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(LinearWeightNorm, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | LinearWeightNorm | false | 14,613 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
GaussianKernel | # 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
assert_size_stride = torch._C._dynamo.guards.assert_... | amberhuang01/LearningFromFactCheckers | GaussianKernel | false | 18,299 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
sSE | # 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... | mattroz/yatopi | sSE | false | 3,988 | [
"MIT"
] | 0 | 278bac6f3d2f13916ae9d43309b9f38b608426bd | https://github.com/mattroz/yatopi/tree/278bac6f3d2f13916ae9d43309b9f38b608426bd |
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.... | chenghaoliu89/TSForecasting_FT | PositionwiseFeedForward | false | 9,995 | [
"MIT"
] | 0 | e29227e67f754919672eab9002a1b37b13ed28a0 | https://github.com/chenghaoliu89/TSForecasting_FT/tree/e29227e67f754919672eab9002a1b37b13ed28a0 |
BertLayerNorm | import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AdrianVandierAst/fast-bert | BertLayerNorm | false | 8,854 | [
"Apache-2.0"
] | 0 | f5adb426accee4a1882cdd4372fced4ef922c978 | https://github.com/AdrianVandierAst/fast-bert/tree/f5adb426accee4a1882cdd4372fced4ef922c978 |
FaceMask | import torch
import torch.nn as nn
import torch.nn.functional as F
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
class FaceMask(nn.Module):
def __init__(self, input_size, out_size):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | VrajeshPatel20/FaceMask-Detection | FaceMask | false | 11,958 | [
"MIT"
] | 0 | 1527f47a94a1b40b470eab633cf4a655c9a3e44e | https://github.com/VrajeshPatel20/FaceMask-Detection/tree/1527f47a94a1b40b470eab633cf4a655c9a3e44e |
LevelVariabilityLoss | import torch
import torch.nn as nn
class LevelVariabilityLoss(nn.Module):
""" Level Variability Loss
Computes the variability penalty for the level.
Parameters
----------
levels: tensor with shape (batch, n_time)
levels obtained from exponential smoothing component of ESRNN
level_variability_penalt... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FedericoGarza/esrnn_torch | LevelVariabilityLoss | false | 11,428 | [
"MIT"
] | 0 | 9f28f38e27dc0ba12cc965e60f7e08e635c8b19d | https://github.com/FedericoGarza/esrnn_torch/tree/9f28f38e27dc0ba12cc965e60f7e08e635c8b19d |
Parseval_Conv2d | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class Parseval_Conv2d(nn.Conv2d):
def forward(self, input):
new_weight = self.weight / np.sqrt(2 * self.kernel_size[0] * self.
kernel_size[1] + 1)
return F.conv2d(input, new_weight, self.bias, self.s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | cadurosar/laplacian_networks | Parseval_Conv2d | false | 6,371 | [
"MIT"
] | 1 | 27f6f2d7145426b38f578e9c1beecae3e7392f1b | https://github.com/cadurosar/laplacian_networks/tree/27f6f2d7145426b38f578e9c1beecae3e7392f1b |
AttDistance | # 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... | ishine/NISQA | AttDistance | false | 15,632 | [
"MIT"
] | 223 | 2c8917f30c4e4bbca3a48e9852301f1e2480a741 | https://github.com/ishine/NISQA/tree/2c8917f30c4e4bbca3a48e9852301f1e2480a741 |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
def __init__(self, epsilon=1e-09):
"""Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难, 不太稳定
paper: Dice Loss for Data-imbalanced NLP Tasks
url: https://arxiv.org/pdf/1911.02855.pdf
args:
reduction: str, Specifies the reduct... | 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... | dumpmemory/Pytorch-NLU | DiceLoss | false | 15,240 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
OverHaulLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
class LabelSmoothingLoss(nn.Module):
def __init__(self, classes, smoothing=0.0, dim=-1):
super(LabelSmoothingLoss, self).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.cls = class... | 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
... | Lee-Gihun/Micronet_GSJ | OverHaulLoss | false | 8,460 | [
"MIT"
] | 12 | 72289bb66507b6c3b4d14f2e5916dec718a1b198 | https://github.com/Lee-Gihun/Micronet_GSJ/tree/72289bb66507b6c3b4d14f2e5916dec718a1b198 |
TextureSegmentation | # 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.... | paucarre/staal | TextureSegmentation | false | 4,123 | [
"MIT"
] | 0 | 1635e514f0ed978a08c078afd258980bcb6f0cec | https://github.com/paucarre/staal/tree/1635e514f0ed978a08c078afd258980bcb6f0cec |
M1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, same_padding
=False, stride=1, relu=True, bn=False):
super(Conv2D, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Juggernaut93/SSH-pytorch | M1 | false | 13,986 | [
"MIT"
] | 63 | 8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 | https://github.com/Juggernaut93/SSH-pytorch/tree/8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 |
complex_relu_layer | import torch
import torch.nn as nn
class complex_relu_layer(nn.Module):
def __init__(self):
super(complex_relu_layer, self).__init__()
def complex_relu(self, real, img):
mask = 1.0 * (real >= 0)
return mask * real, mask * img
def forward(self, real, img=None):
if img is ... | 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... | RemyLau/SimpleMagNet | complex_relu_layer | false | 2,742 | [
"MIT"
] | 0 | ee3cc5fc9a7793d2e2cf5a4b635fb690bb5b988e | https://github.com/RemyLau/SimpleMagNet/tree/ee3cc5fc9a7793d2e2cf5a4b635fb690bb5b988e |
SymLinear | # 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
import torch.utils.data
import torch.nn as nn
from torch.nn.paramete... | Waasem/graph2nn | SymLinear | false | 14,563 | [
"MIT"
] | 133 | b112eb6c6805a1813e433442b0b1f5cabb4ad1a2 | https://github.com/Waasem/graph2nn/tree/b112eb6c6805a1813e433442b0b1f5cabb4ad1a2 |
MegatronGelu | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | RyanUnderhill/onnxruntime | MegatronGelu | false | 11,816 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
h_sigmoid | # 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... | EileenWang90/mmpose | h_sigmoid | false | 2,173 | [
"Apache-2.0"
] | 0 | 3fa1328a3b6351bf9b35df60d4d959973a6f8a71 | https://github.com/EileenWang90/mmpose/tree/3fa1328a3b6351bf9b35df60d4d959973a6f8a71 |
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 as nn
assert_... | Lauu1023/torchdiffeq | Decoder | false | 9,354 | [
"MIT"
] | 0 | f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 | https://github.com/Lauu1023/torchdiffeq/tree/f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 |
GumbelQuantize | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
class GumbelQuantize(nn.Module):
"""
Reference:
Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
https://arxiv.org/abs/1611.01144
"""
def __init__(self, hidden_channel, n_e, e_dim, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | PeikeLi/pytorch-vector-quantization | GumbelQuantize | false | 17,823 | [
"MIT"
] | 6 | 48ce6a74ec56b9d8c11dde2cd35b055a925c3070 | https://github.com/PeikeLi/pytorch-vector-quantization/tree/48ce6a74ec56b9d8c11dde2cd35b055a925c3070 |
VarianceNorm2d | import torch
import torch.nn as nn
class VarianceNorm2d(nn.Module):
def __init__(self, num_features, bias=False):
super().__init__()
self.num_features = num_features
self.bias = bias
self.alpha = nn.Parameter(torch.zeros(num_features))
self.alpha.data.normal_(1, 0.02)
... | 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_... | Sriram-Ravula/Inverse_Meta | VarianceNorm2d | false | 2,852 | [
"MIT"
] | 0 | c6c1e4ae0d670093156249c60d74373b22d61f01 | https://github.com/Sriram-Ravula/Inverse_Meta/tree/c6c1e4ae0d670093156249c60d74373b22d61f01 |
CCAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Softmax
from torchvision.transforms import *
class CCAMDec(Module):
"""
CCAM decoding module
"""
def __init__(self):
super(CCAMDec, self).__init__()
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tousifulhaque/DANet | CCAMDec | false | 4,464 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
return inputs.view((in_size[0],... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AwaleSajil/BiSeNet | GlobalAvgPool2d | false | 11,224 | [
"MIT"
] | 0 | 2724941ef4052224c5581e6e42389e71a7c5cd5d | https://github.com/AwaleSajil/BiSeNet/tree/2724941ef4052224c5581e6e42389e71a7c5cd5d |
SpatialGatherModule | # 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.... | AlexanderDokuchaev/mmsegmentation | SpatialGatherModule | false | 11,205 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
CustomInverse | # 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... | QPC-database/onnxruntime-extensions | CustomInverse | false | 5,721 | [
"MIT"
] | 1 | 7fd96c8e9700425335b479ca042b16fe92f8b8e8 | https://github.com/QPC-database/onnxruntime-extensions/tree/7fd96c8e9700425335b479ca042b16fe92f8b8e8 |
ResidualDenseBlock_3C | # 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 import Parameter
assert_size_stride = torch.... | LenKerr/Semantic-Colorization-GAN | ResidualDenseBlock_3C | false | 5,536 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
RWKV_TimeMix | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RWKV_TimeMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
assert config.n_attn % config.n_head == 0
self.layer_id = layer_id
self.ctx_len = config.ctx_len
self.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.... | YUASDS/AI-Writer | RWKV_TimeMix | false | 6,013 | [
"BSD-3-Clause"
] | 1 | 6ec1e9548802ed5b5a2f1fd297595a52cb605266 | https://github.com/YUASDS/AI-Writer/tree/6ec1e9548802ed5b5a2f1fd297595a52cb605266 |
GAT | # 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.... | RidongHan/GHE-LPC | GAT | false | 17,852 | [
"MIT"
] | 4 | 2a10f423d747aa28560a3bcbf29f7ec87422beb8 | https://github.com/RidongHan/GHE-LPC/tree/2a10f423d747aa28560a3bcbf29f7ec87422beb8 |
KeyValueAttention | # 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.... | ChrisGeishauser/ConvLab-2 | KeyValueAttention | false | 2,258 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
PReLU | # 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... | yifanpu001/PytorchToCaffe | PReLU | false | 4,715 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
TReLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - 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... | archiroid003/ICCV2019-LearningToPaint | TReLU | false | 12,110 | [
"MIT"
] | 0 | 4b5fc263e4843c159a61e5956956b3f7812693f8 | https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8 |
EqualLinear | from torch.autograd import Function
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.nn as nn
from torch... | cyysc1998/EDVRDarts | EqualLinear | false | 6,512 | [
"MIT"
] | 1 | 201badbc8c6469b519647a8869c3782ebe1176cf | https://github.com/cyysc1998/EDVRDarts/tree/201badbc8c6469b519647a8869c3782ebe1176cf |
LSoftLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Vanova/argus-freesound | LSoftLoss | false | 11,948 | [
"MIT"
] | 0 | 55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d | https://github.com/Vanova/argus-freesound/tree/55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d |
SqueezeExcite | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | Capetian/FaceX-Zoo | SqueezeExcite | false | 4,988 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
MinibatchStdDev | import torch
class MinibatchStdDev(torch.nn.Module):
"""
Concatenate a constant statistic calculated across the minibatch to each pixel location (i, j) as a new channel.
Here the standard deviation averaged over channels and locations. This is to increase variation of images produced
by the generator.... | 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... | leogeier/dl-2020-prog-gan | MinibatchStdDev | false | 3,901 | [
"MIT"
] | 0 | 12f28353548188af31cc14ee18a5444ad3d95a0c | https://github.com/leogeier/dl-2020-prog-gan/tree/12f28353548188af31cc14ee18a5444ad3d95a0c |
A2CCritic | import torch
import torch as t
import torch.nn as nn
class A2CCritic(nn.Module):
def __init__(self, state_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state):
v = t.relu(self.fc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | iffiX/machin | A2CCritic | false | 15,587 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
TorchGloVeLoss | import torch
import torch.nn as nn
import torch.utils.data
class TorchGloVeLoss(nn.Module):
def __init__(self):
super().__init__()
self.reduction = 'sum'
def forward(self, diffs, weights):
return torch.sum(0.5 * torch.mul(weights, diffs ** 2))
def get_inputs():
return [torch.ra... | 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... | ammarhusain/cs224u | TorchGloVeLoss | false | 6,180 | [
"Apache-2.0"
] | 1 | bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 | https://github.com/ammarhusain/cs224u/tree/bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 |
ECA | # 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... | Jiannan-Liu/nCoVSegNet | ECA | false | 17,490 | [
"MIT"
] | 5 | 7543e68edff011a7f7b694c97cf0f185d441fd6b | https://github.com/Jiannan-Liu/nCoVSegNet/tree/7543e68edff011a7f7b694c97cf0f185d441fd6b |
ScoreCap | # 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.nn
import torch.optim
assert_size_stride = torch._C._dy... | BerenLuthien/ReAgent | ScoreCap | false | 13,382 | [
"BSD-3-Clause"
] | 1,156 | 52f666670a7fa03206812ef48949f6b934d400f7 | https://github.com/BerenLuthien/ReAgent/tree/52f666670a7fa03206812ef48949f6b934d400f7 |
BCEFocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class BCEFocalLoss(nn.Module):
def __init__(self, alpha=-1, gamma=2.0, reduction='mean'):
super(BCEFocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Stochastic-Adventure/ClinicalTransformerRelationExtraction | BCEFocalLoss | false | 14,440 | [
"MIT"
] | 78 | eef956bbfbd64b008014ef7cac5f818087816725 | https://github.com/Stochastic-Adventure/ClinicalTransformerRelationExtraction/tree/eef956bbfbd64b008014ef7cac5f818087816725 |
IndepAnisotropicGaussianUVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | BUPT-PRIV/detectron2 | IndepAnisotropicGaussianUVLoss | false | 11,239 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
LeNet_300_100 | # 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_... | htt-trangtran/smg | LeNet_300_100 | false | 6,820 | [
"MIT"
] | 1 | b7a49055e7d48ec456bac67ab473db2183d2f597 | https://github.com/htt-trangtran/smg/tree/b7a49055e7d48ec456bac67ab473db2183d2f597 |
MinPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert... | BeautyOfWeb/OPP_Analysis | MinPool | false | 141 | [
"MIT"
] | 0 | 59b2dbc91e07fc14b3a130bff6fadaa19cd36b42 | https://github.com/BeautyOfWeb/OPP_Analysis/tree/59b2dbc91e07fc14b3a130bff6fadaa19cd36b42 |
EnergyEstimateWidthRescale | import torch
from torch import nn as nn
from torch.nn.parameter import Parameter
class EnergyEstimateWidthRescale(nn.Module):
def __init__(self, scales):
super(EnergyEstimateWidthRescale, self).__init__()
self.scales = Parameter(torch.tensor(scales, dtype=torch.float32),
requires_grad... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_st... | zhanhuijing/ECC_PYCHARM | EnergyEstimateWidthRescale | false | 4,656 | [
"MIT"
] | 0 | c5e8fb747d70a2548e9866356f8dacc8df26a077 | https://github.com/zhanhuijing/ECC_PYCHARM/tree/c5e8fb747d70a2548e9866356f8dacc8df26a077 |
AttentionConv | # 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.... | jhvics1/pytorch-stand-alone-self-attention | AttentionConv | false | 10,307 | [
"MIT"
] | 0 | 77375d99250ab9d8089e73bd4803afae30843748 | https://github.com/jhvics1/pytorch-stand-alone-self-attention/tree/77375d99250ab9d8089e73bd4803afae30843748 |
LSoftLoss | # 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... | advian123/kaggle-birdsong-recognition | LSoftLoss | false | 9,933 | [
"MIT"
] | 0 | a4ca8ab81e166b919452fb5d6ca4c2912c65e904 | https://github.com/advian123/kaggle-birdsong-recognition/tree/a4ca8ab81e166b919452fb5d6ca4c2912c65e904 |
GatedLinear | import torch
import torch.nn as nn
import torch.utils.data
class GatedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(GatedLinear, self).__init__()
self.layer_f = nn.Linear(in_features, out_features)
self.layer_g = nn.Linear(in_features, out_features)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ClaraBing/ffjord | GatedLinear | false | 13,521 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
SoftMaxLayer | # 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
... | nicofirst1/lucent | SoftMaxLayer | false | 12,913 | [
"Apache-2.0"
] | 0 | 1e249918e91cc04117368826cd7a192bd8cf2046 | https://github.com/nicofirst1/lucent/tree/1e249918e91cc04117368826cd7a192bd8cf2046 |
GTXCrossAttentionLayer | # 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.... | rsgit95/med_kg_txt_multimodal | GTXCrossAttentionLayer | false | 4,220 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
CELoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class CELoss(nn.Module):
def __init__(self, ratio=1, weight=None, size_average=None,
ignore_index=-100, reduce=None, reduction='mean'):
super(CELoss, self).__init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Dogacel/mmfashion | CELoss | false | 11,417 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
AGELU | # 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 math
import torch.utils.data
import torch.cuda
import torch.utils.checkp... | Dan-hbd/NMTGMinor | AGELU | false | 5,146 | [
"MIT"
] | 1 | 84e59ac8391ee78852d7c71afc60c3c8b8e3d44d | https://github.com/Dan-hbd/NMTGMinor/tree/84e59ac8391ee78852d7c71afc60c3c8b8e3d44d |
CMVN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ana-kuznetsova/s3prl | CMVN | false | 6,196 | [
"Apache-2.0"
] | 1 | 1fd3309f693f9cd765f56b12375ed0e7c41ef093 | https://github.com/ana-kuznetsova/s3prl/tree/1fd3309f693f9cd765f56b12375ed0e7c41ef093 |
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | kristinakupf/FeatureLearningRotNet | GlobalAveragePooling | false | 12,686 | [
"MIT"
] | 0 | d495bcfaed3e7a3ca92b7434f8ad6d7584ab173d | https://github.com/kristinakupf/FeatureLearningRotNet/tree/d495bcfaed3e7a3ca92b7434f8ad6d7584ab173d |
GroupScaling1D | import torch
from torch import nn
class GroupScaling1D(nn.Module):
"""Scales inputs by the second moment for the entire layer."""
def __init__(self, eps=1e-05, group_num=4):
super(GroupScaling1D, self).__init__()
self.eps = eps
self.group_num = group_num
def extra_repr(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.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Piki1989/spacetimeformer | GroupScaling1D | false | 14,177 | [
"MIT"
] | 209 | 7e0caf17dd03e5d25e2766c4f7132805779bcc40 | https://github.com/Piki1989/spacetimeformer/tree/7e0caf17dd03e5d25e2766c4f7132805779bcc40 |
RNN | # 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_... | Thytu/earthquakePrediction | RNN | false | 11,942 | [
"MIT"
] | 0 | 95777022e492bd21aa2107c2b5af7a80b38abc2f | https://github.com/Thytu/earthquakePrediction/tree/95777022e492bd21aa2107c2b5af7a80b38abc2f |
ListEnvEncoder | import torch
import torch.nn.functional as F
import torch.nn as nn
class ListEnvEncoder(nn.Module):
"""
Implement an encoder (f_enc) specific to the List environment. It encodes observations e_t into
vectors s_t of size D = encoding_dim.
"""
def __init__(self, observation_dim, encoding_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | geektoni/AlphaNPI | ListEnvEncoder | false | 3,532 | [
"MIT"
] | 0 | ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 | https://github.com/geektoni/AlphaNPI/tree/ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 |
TagLineLoss | import torch
from torch import nn
class TagLineLoss(nn.Module):
def __init__(self):
super(TagLineLoss, self).__init__()
self.criterion = torch.nn.CrossEntropyLoss()
def forward(self, output, target):
return self.criterion(input=output, target=target)
def get_inputs():
return [t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Benjamintdk/DSAI-Project | TagLineLoss | false | 8,839 | [
"Apache-2.0"
] | 0 | 684b74fcef43972e3f4d308f006fb3b4c8191b18 | https://github.com/Benjamintdk/DSAI-Project/tree/684b74fcef43972e3f4d308f006fb3b4c8191b18 |
CosineLoss | import torch
import torch.nn.functional as F
class CosineLoss(torch.nn.Module):
def __init__(self):
super(CosineLoss, self).__init__()
self.metrics = lambda x, y: 1 - torch.mean(F.cosine_similarity(x, y,
dim=-1))
def forward(self, x, label):
return self.metrics(x, label)
... | 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.functional a... | ackness/eth-xgaze-estimator | CosineLoss | false | 9,644 | [
"MIT"
] | 0 | b617cda6505885942c81b7f2d41399b62985b9a7 | https://github.com/ackness/eth-xgaze-estimator/tree/b617cda6505885942c81b7f2d41399b62985b9a7 |
UNETWithoutPooling | import torch
from torch import nn
class UNETWithoutPooling(nn.Module):
"""UNET without pooling"""
def __init__(self):
super(UNETWithoutPooling, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | quenting44/semantic_segmentation | UNETWithoutPooling | false | 10,824 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
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.nn as nn
import torch.optim
import torch._utils
import torch.nn
as... | ModelTC/EOD | GELU | false | 14,060 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import save as save
from torch import load as load
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l1 = nn.Linear(784, 520)
self.l2 = nn.Linear(520, 320)
self.l3 = nn.Linear(32... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | UoA-sjer215/2021-python-41 | Net | false | 2,941 | [
"Apache-2.0"
] | 0 | b27094f5915395636fa85ad6a4a9751c7224c345 | https://github.com/UoA-sjer215/2021-python-41/tree/b27094f5915395636fa85ad6a4a9751c7224c345 |
BackprojectDepth | import torch
import torch.nn as nn
class BackprojectDepth(nn.Module):
"""Layer to transform a depth image into a point cloud
"""
def __init__(self, batch_size, height, width):
super(BackprojectDepth, self).__init__()
self.batch_size = batch_size
self.height = height
self.w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HalleyJiang/PLNet | BackprojectDepth | false | 8,228 | [
"MIT"
] | 16 | a02bd5f343b9e4766891fd234e3a338c1eaa26ff | https://github.com/HalleyJiang/PLNet/tree/a02bd5f343b9e4766891fd234e3a338c1eaa26ff |
CE_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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | BlakeDai/FedML-test | CE_Loss | false | 9,205 | [
"Apache-2.0"
] | 0 | 3cb9a7234f3f0294f3137e4be572153ba7b62f8f | https://github.com/BlakeDai/FedML-test/tree/3cb9a7234f3f0294f3137e4be572153ba7b62f8f |
outconv | # 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.... | ExplorativeEngineering/LFMNet2 | outconv | false | 13,661 | [
"Apache-2.0"
] | 46 | 3f190be0f047b9e05c69b0a11f99218fd4fc510c | https://github.com/ExplorativeEngineering/LFMNet2/tree/3f190be0f047b9e05c69b0a11f99218fd4fc510c |
PriorDiscriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class PriorDiscriminator(nn.Module):
"""The prior discriminator class.
This discriminate between a vector drawn from random uniform,
and the vector y obtained as output of the encoder.
It enforces y to be close to a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | neuralsyn/self-supervised-relational-reasoning | PriorDiscriminator | false | 16,164 | [
"MIT"
] | 130 | 6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 | https://github.com/neuralsyn/self-supervised-relational-reasoning/tree/6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 |
DQN | # 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... | TejaswiniMedi/DRL | DQN | false | 5,879 | [
"MIT"
] | 1 | d4a694c5e505822e6e8627be52afd0ccc60f80ef | https://github.com/TejaswiniMedi/DRL/tree/d4a694c5e505822e6e8627be52afd0ccc60f80ef |
GRUCell | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | OlhaKi/PySyft | GRUCell | false | 5,688 | [
"Apache-2.0"
] | 1 | c9e16835ba0f05c3ff391e17a33d56a5c2ceb459 | https://github.com/OlhaKi/PySyft/tree/c9e16835ba0f05c3ff391e17a33d56a5c2ceb459 |
ActionAttentionV3 | # 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 ... | footoredo/pymarl | ActionAttentionV3 | false | 3,505 | [
"Apache-2.0"
] | 0 | 9c62dda7a7ed984e020f2cafab93601342305af2 | https://github.com/footoredo/pymarl/tree/9c62dda7a7ed984e020f2cafab93601342305af2 |
L2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | eight0153/flownet2-pytorch | L2 | false | 3,453 | [
"Apache-2.0"
] | 0 | cc2964233cd18c8db05d1751281c6ab9d3165da6 | https://github.com/eight0153/flownet2-pytorch/tree/cc2964233cd18c8db05d1751281c6ab9d3165da6 |
SurfaceLoss | import torch
import torch.nn as nn
class SurfaceLoss(nn.Module):
def __init__(self, epsilon=1e-05, softmax=True):
super(SurfaceLoss, self).__init__()
self.weight_map = []
def forward(self, x, distmap):
x = torch.softmax(x, dim=1)
self.weight_map = distmap
score = x.fl... | 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
... | ArmandB/RITnet | SurfaceLoss | false | 76 | [
"MIT"
] | 0 | afe9524fdd795982c7e52761da68af2dfda589ea | https://github.com/ArmandB/RITnet/tree/afe9524fdd795982c7e52761da68af2dfda589ea |
ReviewClassifier | import torch
import torch.nn.functional as F
import torch.nn as nn
class ReviewClassifier(nn.Module):
def __init__(self, n_feature):
super(ReviewClassifier, self).__init__()
self.lf = nn.Linear(n_feature, 1, dtype=torch.float32)
def forward(self, x):
out = self.lf(x)
out = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AnissHal/tal | ReviewClassifier | false | 1,949 | [
"MIT"
] | 0 | 6e96ffa367be6da54383ae9e6b0f56f7b5cf9a92 | https://github.com/AnissHal/tal/tree/6e96ffa367be6da54383ae9e6b0f56f7b5cf9a92 |
GaussianSample | import torch
import torch.nn as nn
class Stochastic(nn.Module):
"""
Base stochastic layer that uses the
reparametrization trick [Kingma 2013]
to draw a sample from a distribution
parametrised by mu and log_var.
"""
def reparametrize(self, mu, logvar):
epsilon = torch.randn(mu.size... | 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._inductor.runtime.triton_helpers import math... | ericli0419/SCALEX | GaussianSample | false | 6,656 | [
"MIT"
] | 1 | 2fedbe4c3287cf86de7b786c98122fe45707416e | https://github.com/ericli0419/SCALEX/tree/2fedbe4c3287cf86de7b786c98122fe45707416e |
Autoencoder | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.optim as optim
import torch as T
from torch.nn import Conv2d
from torch.nn import MaxPool2d
from torch.nn import BCELoss
class Autoencoder(Module):
def __init__(self, lr=0.002):
super(Autoencoder, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | ShivangMathur1/Small-Pytorch-Projects | Autoencoder | false | 1,895 | [
"MIT"
] | 0 | aebc6e81103fe2a6830caeedc1b17227e211a6e5 | https://github.com/ShivangMathur1/Small-Pytorch-Projects/tree/aebc6e81103fe2a6830caeedc1b17227e211a6e5 |
RgbaToRgb | import torch
import torch.nn as nn
def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert image from RGBA to RGB.
See :class:`~kornia.color.RgbaToRgb` for details.
Args:
image (torch.Tensor): RGBA Image to be converted to RGB.
Returns:
torch.Tensor: RGB version of the ima... | 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... | connorlee77/kornia | RgbaToRgb | false | 6,473 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | af5b1f76bedf2a7fc0e0da2386b1be3032b6534f | https://github.com/connorlee77/kornia/tree/af5b1f76bedf2a7fc0e0da2386b1be3032b6534f |
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.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | Decoder | false | 8,537 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | cheng-xie/dpgfddagger | Actor | false | 3,282 | [
"MIT"
] | 0 | 5264d5b9e0ab76fc9620da63bcfd78b25dadcbec | https://github.com/cheng-xie/dpgfddagger/tree/5264d5b9e0ab76fc9620da63bcfd78b25dadcbec |
Memory | # 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.... | JoonHong-Kim/T2I_CL | Memory | false | 8,382 | [
"MIT"
] | 35 | c52aa73da903d6e4174eeef2663e5bc1163785b1 | https://github.com/JoonHong-Kim/T2I_CL/tree/c52aa73da903d6e4174eeef2663e5bc1163785b1 |
NaiveTorchNet | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.autograd
import torch.optim as optim
class NaiveTorchNet(nn.Module):
"""A reimplementation of from-scratch NaiveNet using PyTorch"""
def __init__(self, input_nodes, hidden_nodes, output_nodes, learn_rate=0.1
):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Variable
import torch.nn as nn
import torch.autograd
... | deo1/deo1 | NaiveTorchNet | false | 1,836 | [
"MIT"
] | 0 | 36671f12269d3bd662d746e8b9f66c22255c9df7 | https://github.com/deo1/deo1/tree/36671f12269d3bd662d746e8b9f66c22255c9df7 |
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