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 |
|---|---|---|---|---|---|---|---|---|---|---|
KLDLoss | # 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
... | atmacvit/meronymnet | KLDLoss | false | 6,271 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
L1GradientLoss | # 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.... | YDDDDG/3D2Unet | L1GradientLoss | false | 6,009 | [
"MIT"
] | 1 | daca056958fb2ae319dc18a350e04b3cefe0d99f | https://github.com/YDDDDG/3D2Unet/tree/daca056958fb2ae319dc18a350e04b3cefe0d99f |
OneSideInterModalityUpdate | # 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.... | Ruiver/CTCNet | OneSideInterModalityUpdate | false | 17,906 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
SimpleModel | # 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.functional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data... | Jovonni/jukebox | SimpleModel | false | 669 | [
"MIT"
] | 0 | 965a6f78aae67506a6e4fcdb205e2c39132e12e0 | https://github.com/Jovonni/jukebox/tree/965a6f78aae67506a6e4fcdb205e2c39132e12e0 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import numpy as np
import torch.nn as nn
imp... | henryaddison/score_sde_pytorch | Conv2d | false | 12,505 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""
A model layer which implements a simplified version of masked attention, as
introduced by "Attention Is All You Need" (https://arxiv.org/abs/1706.03762).
Usage:
attn = MultiHeadAttention(embed_dim, num_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.... | Michellemingxuan/stanford_cs231n | MultiHeadAttention | false | 11,827 | [
"MIT"
] | 0 | b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 | https://github.com/Michellemingxuan/stanford_cs231n/tree/b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 |
ConvElu | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvElu(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvElu, self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | XiaobingSuper/intel-extension-for-pytorch | ConvElu | false | 9,718 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
RegWeightedL1Loss | # 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... | kuanhungchen/CenterNet-HarDNet | RegWeightedL1Loss | false | 15,868 | [
"MIT"
] | 164 | 050d55a532706d989105982c5bc10f1c89edc8d2 | https://github.com/kuanhungchen/CenterNet-HarDNet/tree/050d55a532706d989105982c5bc10f1c89edc8d2 |
SmoothNetResBlock | import torch
import torch.nn as nn
class SmoothNetResBlock(nn.Module):
"""Residual block module used in SmoothNet.
Args:
in_channels (int): Input channel number.
hidden_channels (int): The hidden feature channel number.
dropout (float): Dropout probability. Default: 0.5
Shape:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ALISCIFP/mmpose | SmoothNetResBlock | false | 2,064 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
DecoderBlock | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
impor... | HugoPopo/robosat.pink | DecoderBlock | false | 2,353 | [
"MIT"
] | 0 | daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d | https://github.com/HugoPopo/robosat.pink/tree/daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d |
GatSymAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
class ConstAttention(nn.Module):
def __init__(self, **kwargs):
super(ConstAttention, self).__init__()
def forward(self, neighbor_vecs, self_vecs):
return 1
class GatAttention(ConstAttention):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_siz... | GraphNAS/GraphNAS | GatSymAttention | false | 13,739 | [
"Apache-2.0"
] | 94 | b4f05bb10b8b96bb9e82344bfae36a23db2431a6 | https://github.com/GraphNAS/GraphNAS/tree/b4f05bb10b8b96bb9e82344bfae36a23db2431a6 |
SmoothL1Loss | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
import torch.multiprocessing
class SmoothL1Loss(nn.Module):
"""Smooth L1 Loss"""
def __init__(self, beta=0.11):
super().__init__()
self.beta = beta
def forward(self, pred, target):
x = (pred - target).a... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.cuda
import torch.distributed
import t... | pkashinkunti/retinanet-examples | SmoothL1Loss | false | 7,465 | [
"BSD-3-Clause"
] | 1 | 30466522c018d4d0bd921485024e871b32ec4e73 | https://github.com/pkashinkunti/retinanet-examples/tree/30466522c018d4d0bd921485024e871b32ec4e73 |
SoftDiceLoss | import torch
import torch.nn as nn
class SoftDiceLoss(nn.Module):
def __init__(self):
super(SoftDiceLoss, self).__init__()
def forward(self, output, label):
probs = output.view(-1)
mask = label.view(-1)
smooth = 1
intersection = torch.sum(probs * mask)
den1 = ... | 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... | tdml13/NiftyNet | SoftDiceLoss | false | 16,531 | [
"Apache-2.0"
] | 1,403 | b35fa19ca307e81d229e2fe8269a417724833da2 | https://github.com/tdml13/NiftyNet/tree/b35fa19ca307e81d229e2fe8269a417724833da2 |
RKDAngleLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class RKDAngleLoss(nn.Module):
"""
Module for calculating RKD Angle Loss
"""
def forward(self, teacher, student, normalize=True):
"""
Forward function
:param teacher (torch.FloatTensor): Prediction made by the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NeelayS/KD_Lib | RKDAngleLoss | false | 2,677 | [
"MIT"
] | 0 | c3f8c7cef76772d14862260e61c1d1c52c58f58e | https://github.com/NeelayS/KD_Lib/tree/c3f8c7cef76772d14862260e61c1d1c52c58f58e |
CoverageAttention | # 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.... | NetEase-GameAI/SARG | CoverageAttention | false | 16,239 | [
"BSD-3-Clause"
] | 53 | 037085794f10439c4e52f57ab0fa042f35d03f62 | https://github.com/NetEase-GameAI/SARG/tree/037085794f10439c4e52f57ab0fa042f35d03f62 |
BinaryTreeComposer | # 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 ... | supunab/Lantern | BinaryTreeComposer | false | 16,520 | [
"BSD-3-Clause"
] | 158 | 932a031816617d71c46653f3b2245129a6a8a7c8 | https://github.com/supunab/Lantern/tree/932a031816617d71c46653f3b2245129a6a8a7c8 |
DirectedGraphConvolution | import torch
import torch.nn as nn
import torch.nn.functional as F
def normalize_adj(adj):
last_dim = adj.size(-1)
rowsum = adj.sum(2, keepdim=True).repeat(1, 1, last_dim)
return torch.div(adj, rowsum)
class DirectedGraphConvolution(nn.Module):
def __init__(self, in_features, out_features):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | naszilla/naszilla | DirectedGraphConvolution | false | 16,145 | [
"Apache-2.0"
] | 112 | 5575cc8c95e79ce5743e8ea7ef53d6da900f8480 | https://github.com/naszilla/naszilla/tree/5575cc8c95e79ce5743e8ea7ef53d6da900f8480 |
MetaLayerNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited f... | 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_... | KIMGEONUNG/multi-memory-siren | MetaLayerNorm | false | 716 | [
"MIT"
] | 0 | b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b | https://github.com/KIMGEONUNG/multi-memory-siren/tree/b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b |
UpConcat2d | # 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_... | rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets | UpConcat2d | false | 7,560 | [
"MIT"
] | 1 | 75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 | https://github.com/rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets/tree/75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 |
P2SActivationLayer | # 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.... | eurecom-asp/raw-pc-darts-anti-spoofing | P2SActivationLayer | false | 6,663 | [
"MIT"
] | 1 | f2dcb5a8fc0cb811328a341a9bd90ffb292adaa1 | https://github.com/eurecom-asp/raw-pc-darts-anti-spoofing/tree/f2dcb5a8fc0cb811328a341a9bd90ffb292adaa1 |
Generator | import torch
import torch.onnx
import torch.nn as nn
def outputActivation(x):
muX = x[:, :, 0:1]
muY = x[:, :, 1:2]
sigX = x[:, :, 2:3]
sigY = x[:, :, 3:4]
rho = x[:, :, 4:5]
sigX = torch.exp(sigX)
sigY = torch.exp(sigY)
rho = torch.tanh(rho)
out = torch.cat([muX, muY, sigX, sigY, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | PhilippeW83440/conv-social-pooling | Generator | false | 17,813 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
idct_8x8 | # 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 itertools
import numpy as np
import torch.nn as nn
assert_size_stride = t... | DazhiZhong/DiffJPEG | idct_8x8 | false | 9,012 | [
"MIT"
] | 0 | e20de92539f31a57906ae4c32a41dc46e774c316 | https://github.com/DazhiZhong/DiffJPEG/tree/e20de92539f31a57906ae4c32a41dc46e774c316 |
act_PRT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Cheeun/FDSR | act_PRT | false | 4,984 | [
"MIT"
] | 1 | 28b1c3c102334c5336038d0a0f6e1fceb393659a | https://github.com/Cheeun/FDSR/tree/28b1c3c102334c5336038d0a0f6e1fceb393659a |
ConcatBlock | import torch
import torch.nn as nn
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HiLab-git/WSL4MIS | ConcatBlock | false | 8,276 | [
"MIT"
] | 29 | 9683e7c7409b95c0ac2169fe7964f6ca04c80d9a | https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a |
Self_Attn | import torch
import torch.nn as nn
class Self_Attn(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim):
super().__init__()
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim //
2, kernel_size=1)
self.key_conv = nn.Conv2d(in_channels=in_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.... | Aympab/DCGAN | Self_Attn | false | 8,875 | [
"Apache-2.0"
] | 0 | 2d5aeb62e33f31fc5bfcfdac8b951cd7ae144b96 | https://github.com/Aympab/DCGAN/tree/2d5aeb62e33f31fc5bfcfdac8b951cd7ae144b96 |
EqualLinear | import torch
from torch import nn
import torch.nn.functional as F
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn.Parameter(torch.zeros(o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Asha-Gutlapalli/StyleGAN2-Art | EqualLinear | false | 16,950 | [
"MIT"
] | 4 | 5a8a8ad61183e82abafe587d755a7fbce28aa8f0 | https://github.com/Asha-Gutlapalli/StyleGAN2-Art/tree/5a8a8ad61183e82abafe587d755a7fbce28aa8f0 |
ReluSquared | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | imflash217/bumblebee | ReluSquared | false | 12,531 | [
"MIT"
] | 0 | 09343d42634aa954cac867f7e426eee260b4df57 | https://github.com/imflash217/bumblebee/tree/09343d42634aa954cac867f7e426eee260b4df57 |
LenCompLoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class LenCompLoss(nn.Module):
def __init__(self):
super(LenCompLoss, self).__init__()
self.loss = nn.L1Loss()
def forward(self, x, y):
loss = self.loss(torch.sum(x), torch.sum(y))
return loss
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | usmanwardag/pytorch-CycleGAN-and-pix2pix | LenCompLoss | false | 4,468 | [
"BSD-3-Clause"
] | 0 | 72f2050600e7821476c9e19fcf8f1973f6a6f78c | https://github.com/usmanwardag/pytorch-CycleGAN-and-pix2pix/tree/72f2050600e7821476c9e19fcf8f1973f6a6f78c |
Bridge | # 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 ... | aiarjun/Monocular-Depth-Estimation | Bridge | false | 18,247 | [
"MIT"
] | 6 | 5989673f1b6d865f822a342448172b374968c234 | https://github.com/aiarjun/Monocular-Depth-Estimation/tree/5989673f1b6d865f822a342448172b374968c234 |
FCNet | # 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 ... | savan77/nni | FCNet | false | 4,277 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mode='replicate'), 2, stride=1)
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Abdul-Mukit/ssp_with_hand_tracking | MaxPoolStride1 | false | 11,155 | [
"MIT"
] | 0 | 04429ac9789283694a9176b94f70ab4e5a8c0727 | https://github.com/Abdul-Mukit/ssp_with_hand_tracking/tree/04429ac9789283694a9176b94f70ab4e5a8c0727 |
Conv2dLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def cal_width_dim_2d(input_dim, kernel_size, stride, padding=1):
return math.floor((input_dim + 2 * padding - kernel_size) / stride + 1)
class Conv2dLayer(nn.Module):
def __init__(self, input_size, in_channel, out_channel, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | jiyanglii/OpenTransformer | Conv2dLayer | false | 15,705 | [
"MIT"
] | 321 | f37cc8cbbc96ddb67082dd2962d09303551010c8 | https://github.com/jiyanglii/OpenTransformer/tree/f37cc8cbbc96ddb67082dd2962d09303551010c8 |
SpatialCrossMapLRN | # 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 import nn
import torch.nn.parallel
import torch.optim
import torch.u... | OrKatz7/kaggle-hubmap | SpatialCrossMapLRN | false | 9,882 | [
"MIT"
] | 0 | 5cf8c5aebe956c256fa7f3db432639e28f29c6a3 | https://github.com/OrKatz7/kaggle-hubmap/tree/5cf8c5aebe956c256fa7f3db432639e28f29c6a3 |
GraphAttentionLayer | # 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.... | Nmegha2601/activitygraph_transformer | GraphAttentionLayer | false | 14,126 | [
"MIT"
] | 63 | 4e21a4ea12527df470b7586d149fa4168a41307c | https://github.com/Nmegha2601/activitygraph_transformer/tree/4e21a4ea12527df470b7586d149fa4168a41307c |
AFTSimple | # 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.... | dumpmemory/aft-pytorch | AFTSimple | false | 15,275 | [
"MIT"
] | 170 | 9a896966481f4042c2882f544d7bb1381e81dca1 | https://github.com/dumpmemory/aft-pytorch/tree/9a896966481f4042c2882f544d7bb1381e81dca1 |
AvgPoolPadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class AvgPoolPadding(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super(AvgPoolPadding, self).__init__()
self.identity = nn.AvgPool2d(stride, stride=stride)
self.num_zeros = num_filters - channels_in
... | 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... | citelab/fastsync | AvgPoolPadding | false | 1,708 | [
"Apache-2.0"
] | 0 | 8e2166f87fc53479b57fef536a971c3a2e6e4309 | https://github.com/citelab/fastsync/tree/8e2166f87fc53479b57fef536a971c3a2e6e4309 |
QuickGELU | import torch
from torch import nn
class QuickGELU(nn.Module):
def forward(self, x: 'torch.Tensor'):
return x * torch.sigmoid(1.702 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | CryhanFang/CLIP2Video | QuickGELU | false | 13,517 | [
"MIT"
] | 113 | e94131800a3a1434f6d00b89b7301d741db8ba06 | https://github.com/CryhanFang/CLIP2Video/tree/e94131800a3a1434f6d00b89b7301d741db8ba06 |
h_sigmoid | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GewelsJI/VPS | h_sigmoid | false | 8,175 | [
"Apache-2.0"
] | 22 | 8cb7f584be3c5fc0941126860f2198cb1d88fc4e | https://github.com/GewelsJI/VPS/tree/8cb7f584be3c5fc0941126860f2198cb1d88fc4e |
ChannelSpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Nightmare4214/FracNet | ChannelSpatialSELayer3D | false | 2,704 | [
"Apache-2.0"
] | 0 | db397adb50f71387155d9d110302a5968f86f756 | https://github.com/Nightmare4214/FracNet/tree/db397adb50f71387155d9d110302a5968f86f756 |
Normalize | import torch
from torch import nn
class Normalize(nn.Module):
""" Ln normalization copied from
https://github.com/salesforce/CoMatch
"""
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TencentYoutuResearch/Classification-SemiCLS | Normalize | false | 17,989 | [
"Apache-2.0"
] | 4 | ceb5546f8d8ba08e18de3b5d9426e6cda177e55e | https://github.com/TencentYoutuResearch/Classification-SemiCLS/tree/ceb5546f8d8ba08e18de3b5d9426e6cda177e55e |
InverseDepthSmoothnessLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | InverseDepthSmoothnessLoss | false | 278 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
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.... | IusztinPaul/yacht | ScaledDotProductAttention | false | 17,441 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 |
SequenceClassifier | # 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 collections import Order... | NaimKabir/hakuna-madata | SequenceClassifier | false | 9,371 | [
"MIT"
] | 0 | b7672fe8e50267adf9d3c65cc31c268364133e9c | https://github.com/NaimKabir/hakuna-madata/tree/b7672fe8e50267adf9d3c65cc31c268364133e9c |
MultiHeadedAttention | # 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.... | AmitMY/joeynmt | MultiHeadedAttention | false | 13,265 | [
"Apache-2.0"
] | 563 | b30d1d53823ced56113def8fb5d5f7905d3c059f | https://github.com/AmitMY/joeynmt/tree/b30d1d53823ced56113def8fb5d5f7905d3c059f |
Scale | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
def get_inputs():
return [torch.rand([4, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AllenPeng0209/SaccadeNet | Scale | false | 7,646 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
WeightedBCE | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCE(nn.Module):
"""Weighted binary cross-entropy.
"""
def __init__(self, size_average=True, reduce=True):
super().__init__()
self.size_average = size_average
... | 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCE | false | 13,307 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
MLP3_hardsig | # 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.... | RuokaiYin/UnarySim | MLP3_hardsig | false | 5,784 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
JSCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | kiminh/mt-dnn | JSCriterion | false | 7,033 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
ScaledDotProduction | import torch
import torch.nn as nn
class ScaledDotProduction(nn.Module):
"""Scaled Dot Production"""
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Softmax(dim=2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jincheng-Sun/Kylearn-pytorch | ScaledDotProduction | false | 639 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
FakeReLUM | import torch
import torch.nn as nn
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
def forward(self, x):
return FakeRe... | 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... | Jay-Roberts/FW-Perturbations | FakeReLUM | false | 9,113 | [
"MIT"
] | 0 | 0960f6116125307cc986f9f19b3c5ab4c15ed535 | https://github.com/Jay-Roberts/FW-Perturbations/tree/0960f6116125307cc986f9f19b3c5ab4c15ed535 |
BCE_Dice | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=1):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | bantiitnab/kaggle-TGS-salt-identification | BCE_Dice | false | 1,517 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
EntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EntropyLoss(nn.Module):
def __init__(self):
super(EntropyLoss, self).__init__()
def forward(self, x):
out = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
out = -1.0 * out.sum(dim=1)
return out.mean()
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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | EntropyLoss | false | 17,737 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
RewardCriterion | import torch
import torch.nn as nn
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
... | 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... | Ago3/VLP | RewardCriterion | false | 8,829 | [
"Apache-2.0"
] | 0 | 4dec0e04b8592f4a74fe66c253dbb92574e7e2ba | https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba |
RMSEFeaturesLoss | import torch
import torch.nn as nn
import torch.utils.data
def rmseOnFeatures(feature_difference):
gt = torch.zeros_like(feature_difference)
return torch.nn.functional.mse_loss(feature_difference, gt,
size_average=False)
class RMSEFeaturesLoss(nn.Module):
def __init__(self):
super(RMSEF... | 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... | RerRayne/learn3d | RMSEFeaturesLoss | false | 14,284 | [
"MIT"
] | 335 | 83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 | https://github.com/RerRayne/learn3d/tree/83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 |
SoftDiceLoss | import torch
import numpy as np
from torch import nn
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=True):
inp = inp.sum(int(ax))
... | 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... | Project-SwaG/igvc-software | SoftDiceLoss | false | 14,243 | [
"MIT"
] | 100 | cfe5ad5ae06199030544560af7e4ebf732cd3004 | https://github.com/Project-SwaG/igvc-software/tree/cfe5ad5ae06199030544560af7e4ebf732cd3004 |
AffineChannel2d | # 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AmorosTech/RP-R-CNN | AffineChannel2d | false | 13,241 | [
"MIT"
] | 78 | 45557a69ae9789e2662e3b937feb7624319a3e73 | https://github.com/AmorosTech/RP-R-CNN/tree/45557a69ae9789e2662e3b937feb7624319a3e73 |
MultiplicationInverse | import torch
import torch.nn
class MultiplicationInverse(torch.nn.Module):
def __init__(self, factor=2):
super(MultiplicationInverse, self).__init__()
self.factor = torch.nn.Parameter(torch.ones(1) * factor)
def forward(self, x):
return x * self.factor
def inverse(self, 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.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | MultiplicationInverse | false | 301 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
PoolFormerBlock | # 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 ... | TranNhiem/solo-learn | PoolFormerBlock | false | 1,168 | [
"MIT"
] | 0 | 7539732b68d153087d09a26a23e1edfdc49bc086 | https://github.com/TranNhiem/solo-learn/tree/7539732b68d153087d09a26a23e1edfdc49bc086 |
GCN | # 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.... | yutaoming/Rare-Category-Detection | GCN | false | 4,693 | [
"MIT"
] | 0 | 76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 | https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 |
SimpleOrModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleOrModule | false | 7,407 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Envelope | import torch
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | THinnerichs/pytorch_geometric | Envelope | false | 11,899 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
mfm | # 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 ... | CFengFeng/face-nn | mfm | false | 4,925 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
NormSoftmaxLoss | import math
import torch
import torch.nn as nn
from torch.nn import Parameter
class NormSoftmaxLoss(nn.Module):
"""
L2 normalize weights and apply temperature scaling on logits.
"""
def __init__(self, dim, num_instances, temperature=0.05):
super(NormSoftmaxLoss, self).__init__()
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kikaitech/classification_metric_learning | NormSoftmaxLoss | false | 15,847 | [
"Apache-2.0"
] | 93 | 6c90cecf8be01eda6efb7f6aa4049d8449ca33f1 | https://github.com/kikaitech/classification_metric_learning/tree/6c90cecf8be01eda6efb7f6aa4049d8449ca33f1 |
Eltwise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Alin1102/Yolov3_Dartnet2Caffe | Eltwise | false | 7,640 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
ReCoNet | # 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.... | irsisyphus/reconet | ReCoNet | false | 15,631 | [
"MIT"
] | 56 | 863acf8dde4d45c8521634af27878fe04f3b2e56 | https://github.com/irsisyphus/reconet/tree/863acf8dde4d45c8521634af27878fe04f3b2e56 |
QuaternionLinear | from torch.nn import Module
import torch
import numpy as np
from numpy.random import RandomState
from torch.nn.parameter import Parameter
def quaternion_init(in_features, out_features, rng, kernel_size=None,
criterion='glorot'):
if kernel_size is not None:
receptive_field = np.prod(kernel_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import numpy as np
from numpy.random import RandomSt... | ispamm/DualQSELD-TCN | QuaternionLinear | false | 3,695 | [
"MIT"
] | 0 | fc5dc8840b4fdd8cb09f8f92e628561417df268a | https://github.com/ispamm/DualQSELD-TCN/tree/fc5dc8840b4fdd8cb09f8f92e628561417df268a |
DotAtte | import math
import torch
from torch import nn
import torch.utils.data
def seq_mask(seq_len, max_len):
"""Create sequence mask.
:param seq_len: list or torch.Tensor, the lengths of sequences in a batch.
:param max_len: int, the maximum sequence length in a batch.
:return mask: torch.LongTensor, [batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | FengZiYjun/fastNLP | DotAtte | false | 5,160 | [
"Apache-2.0"
] | 1 | 3ae73ab0a05d1ceef4a5181516891a8057d7f719 | https://github.com/FengZiYjun/fastNLP/tree/3ae73ab0a05d1ceef4a5181516891a8057d7f719 |
PatchEmbed3D | import torch
import torch.nn.functional as F
import torch.nn as nn
class PatchEmbed3D(nn.Module):
""" Video to Patch Embedding.
Args:
patch_size (int): Patch token size. Default: (2,4,4).
in_chans (int): Number of input video channels. Default: 3.
embed_dim (int): Number of linear proj... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Viditagarwal7479/Video-Swin-Transformer | PatchEmbed3D | false | 18,060 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d |
Actor | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Emily0219/distiller | Actor | false | 5,134 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
CO2Regularizer | import torch
class MemoryBankModule(torch.nn.Module):
"""Memory bank implementation
This is a parent class to all loss functions implemented by the lightly
Python package. This way, any loss can be used with a memory bank if
desired.
Attributes:
size:
Number of keys the memo... | 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
assert_size_stride = torch._... | lightly-ai/lightly | CO2Regularizer | false | 15,911 | [
"MIT"
] | 1,515 | 0b98bda640d13d842fd13f9354271d0cef116ba5 | https://github.com/lightly-ai/lightly/tree/0b98bda640d13d842fd13f9354271d0cef116ba5 |
Normalize | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TencentYoutuResearch/Classification-SemiCLS | Normalize | false | 17,989 | [
"Apache-2.0"
] | 4 | ceb5546f8d8ba08e18de3b5d9426e6cda177e55e | https://github.com/TencentYoutuResearch/Classification-SemiCLS/tree/ceb5546f8d8ba08e18de3b5d9426e6cda177e55e |
GatedConv1d | # 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.onnx
assert_size_stride = torch._C._dynamo.gu... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | GatedConv1d | false | 3,771 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
DiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiscrimNet(nn.Module):
def __init__(self, observatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AswinRetnakumar/Machina | DiscrimNet | false | 13,325 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | devolfnn/mmsegmentation | Model | false | 9,987 | [
"Apache-2.0"
] | 0 | c0dccc1725b80b643419cc008cb93e8dcb4209c8 | https://github.com/devolfnn/mmsegmentation/tree/c0dccc1725b80b643419cc008cb93e8dcb4209c8 |
CombineSlices | # 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
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
import torch.fft
assert_size_stride = to... | Gaskell-1206/fastMRI | CombineSlices | false | 13,695 | [
"MIT"
] | 815 | 1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c | https://github.com/Gaskell-1206/fastMRI/tree/1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c |
SimpleConvTranspose2dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleConvTranspose2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, output_padding=0, dilation=1,
groups=1):
super(SimpleConvTranspose2dModule, self).__init__()
self.str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.jit
import torch... | YaronBenAtar/glow | SimpleConvTranspose2dModule | false | 14,644 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
FFNet | # 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_... | Maosef/easy-to-hard | FFNet | false | 8,525 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
Attention | # 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.... | IndexFziQ/ASER | Attention | false | 2,381 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
SaN | import torch
import torch.nn as nn
from collections import OrderedDict
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
x = x.view(x.size(0), -1)
return x
class L2Normalization(nn.Module):
def __init__(self):
super(L2N... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jiangtong-Li/ZHSIR | SaN | false | 17,568 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
Softmax and sigmoid focal loss
"""
def __init__(self, num_labels, activation_type='softmax', gamma=2.0,
alpha=0.25, epsilon=1e-09):
super(FocalLoss, self).__init__()
self.num_lab... | 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
... | Raiselimit/TorchBlocks | FocalLoss | false | 5,747 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
AdapterLayer | # 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 ... | Chriskuei/FedMatch | AdapterLayer | false | 18,372 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
DropBlockT_1d | import torch
import torch.nn as nn
class DropBlockT_1d(nn.Module):
def __init__(self, keep_prob=0.9):
super(DropBlockT_1d, self).__init__()
self.keep_prob = keep_prob
def forward(self, input, mask):
n, c, t, v = input.size()
input1 = input.permute(0, 1, 3, 2).contiguous().vie... | 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... | Levigty/AimCLR | DropBlockT_1d | false | 8,440 | [
"MIT"
] | 25 | 6cd73767f17748792508647355fa324fa63e235d | https://github.com/Levigty/AimCLR/tree/6cd73767f17748792508647355fa324fa63e235d |
STFullyConnected | import time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cthoyt/DrugEx | STFullyConnected | false | 1,826 | [
"MIT"
] | 0 | 9e4d31adb2c65d0afc852948f502c79dcf8308a3 | https://github.com/cthoyt/DrugEx/tree/9e4d31adb2c65d0afc852948f502c79dcf8308a3 |
DoubleConvBlock | # 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_... | manipopopo/C5 | DoubleConvBlock | false | 16,002 | [
"Apache-2.0"
] | 51 | 154eb38c330e65476ddb77836948a28237f23c88 | https://github.com/manipopopo/C5/tree/154eb38c330e65476ddb77836948a28237f23c88 |
WSConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | SongsLearning/Machine-Learning-Collection | WSConv2d | false | 1,087 | [
"MIT"
] | 0 | a8dff83969f67d37f70a89db06b851057d2da539 | https://github.com/SongsLearning/Machine-Learning-Collection/tree/a8dff83969f67d37f70a89db06b851057d2da539 |
ApplySingleAttention | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zhanwenchen/Scene-Graph-Benchmark.pytorch | ApplySingleAttention | false | 4,660 | [
"MIT"
] | 0 | c86475bcbdaefcc1656a2890194355c2b32aa694 | https://github.com/zhanwenchen/Scene-Graph-Benchmark.pytorch/tree/c86475bcbdaefcc1656a2890194355c2b32aa694 |
Actor | import torch
import torch.nn as nn
from collections import OrderedDict
class Actor(nn.Module):
def __init__(self, state_size, action_size, actor_fc_sizes=[256, 128, 64]):
super(Actor, self).__init__()
sequence_dict_actor = OrderedDict()
sequence_dict_actor['fc0'] = nn.Linear(state_size, 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.triton_helpers import libdevice
import torch.nn as ... | kurohi/deepreinforcement-udacity | Actor | false | 3,869 | [
"MIT"
] | 0 | ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 | https://github.com/kurohi/deepreinforcement-udacity/tree/ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 |
group | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, mode=1):
"""
mfm
:param in_channels: in channel
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CFengFeng/face-nn | group | false | 4,935 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
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
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | CFengFeng/face-nn | GlobalAvgPool2d | false | 4,918 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
UpSample | # 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_... | jpjuvo/deepfake-video-detector | UpSample | false | 6,987 | [
"MIT"
] | 1 | 7c5ea5f36277ff5405d8466e48e68d00a085fa7e | https://github.com/jpjuvo/deepfake-video-detector/tree/7c5ea5f36277ff5405d8466e48e68d00a085fa7e |
ToRGB | # 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 numpy as np
from torch import nn
import torch.nn.functional a... | HubBucket-Team/annotated_deep_learning_paper_implementations | ToRGB | false | 5,323 | [
"MIT"
] | 1 | 4a9716b01e336c57739dfdbdd90648276b53c433 | https://github.com/HubBucket-Team/annotated_deep_learning_paper_implementations/tree/4a9716b01e336c57739dfdbdd90648276b53c433 |
Lift | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_normal
def ZeroInitializer(param):
shape = param.size()
init = np.zeros(shape).astype(np.float32)
param.data.set_(torch.from_numpy(init))
def Linear(initializer=kaiming_normal, bias_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | NLP-Discourse-SoochowU/rst_dp2019Bottom2Up | Lift | false | 5,626 | [
"MIT"
] | 1 | ac1624127c9c8a3301685193ac8239357e01f6ca | https://github.com/NLP-Discourse-SoochowU/rst_dp2019Bottom2Up/tree/ac1624127c9c8a3301685193ac8239357e01f6ca |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ArrowLuo/GRACE | BertAttention | false | 8,776 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
CFRB | # 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 collections import Order... | WestCityInstitute/KAIR | CFRB | false | 14,622 | [
"MIT"
] | 1,521 | 3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 | https://github.com/WestCityInstitute/KAIR/tree/3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 |
MLPClassifier | import torch
import torch.nn as nn
class MLPClassifier(nn.Module):
def __init__(self, embedding_dim, label_size, hidden_dim):
super(MLPClassifier, self).__init__()
self.layer1 = torch.nn.Linear(embedding_dim, hidden_dim)
self.relu = torch.nn.ReLU()
self.layer2 = torch.nn.Linear(hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | UKPLab/curriculum-annotation | MLPClassifier | false | 9,555 | [
"Apache-2.0"
] | 0 | 1d6ca490ea180019bb09d1d3818874f4321d4d0f | https://github.com/UKPLab/curriculum-annotation/tree/1d6ca490ea180019bb09d1d3818874f4321d4d0f |
BaselineTokenCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class BaselineTokenCNN(nn.Module):
def __init__(self, num_classes):
super(BaselineTokenCNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=7)
self.pool1 = nn.MaxPool2d(kernel_size=2, str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Jesse-mk/10617_Project | BaselineTokenCNN | false | 9,167 | [
"MIT"
] | 0 | 2290e582fddc74f2f2f3e64e25f33a3bef6b1841 | https://github.com/Jesse-mk/10617_Project/tree/2290e582fddc74f2f2f3e64e25f33a3bef6b1841 |
SelfAttnMatch | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x +
0.044715 * torch.pow(x, 3))))
class SelfAttnMatch(nn.Module):
"""Given sequences X and Y, match seq... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MobtgZhang/MWMLNet | SelfAttnMatch | false | 5,622 | [
"MIT"
] | 1 | 125bb39935916b6b4be505c51cb6a04eb49b96d0 | https://github.com/MobtgZhang/MWMLNet/tree/125bb39935916b6b4be505c51cb6a04eb49b96d0 |
Multi_feature_fusing | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
def l2norm(X, dim=-1, eps=1e-12):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class Multi_feature_fusing(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
import torch.nn.init
assert_size_strid... | AndresPMD/semantic_adaptive_margin | Multi_feature_fusing | false | 7,657 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
DBLoss | # 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 numpy as np
fro... | SURFZJY/Real-time-Text-Detection | DBLoss | false | 14,374 | [
"Apache-2.0"
] | 65 | b76ee8d840b1fcebf7b9545402907416c7daf24e | https://github.com/SURFZJY/Real-time-Text-Detection/tree/b76ee8d840b1fcebf7b9545402907416c7daf24e |
SoftQNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003):
super(SoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
self.linear2 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | FTC-8856/SAC | SoftQNetwork | false | 9,041 | [
"MIT"
] | 0 | 98898d2c4b2ae99b74a8b5a6934d5d3cb91fe5f4 | https://github.com/FTC-8856/SAC/tree/98898d2c4b2ae99b74a8b5a6934d5d3cb91fe5f4 |
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