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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
assert_... | BradyFU/DVG | mfm | false | 13,417 | [
"MIT"
] | 102 | 53fd50cdc51d783b33394726b8f8a2b2216f157b | https://github.com/BradyFU/DVG/tree/53fd50cdc51d783b33394726b8f8a2b2216f157b |
ContrastiveEmbeddingLoss | import torch
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class ContrastiveEmbeddingLoss(nn.Module):
"""The Contrastive embedding loss.
It has been propo... | 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... | Ditwoo/catalyst | ContrastiveEmbeddingLoss | false | 5,076 | [
"Apache-2.0"
] | 1 | 3126390f9f679ebcfedbe01707b416678a2732ac | https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac |
ConvNorm | import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | CODEJIN/TacoSinger | ConvNorm | false | 4,934 | [
"MIT"
] | 1 | af58a8f4e8b20e8817990f28a3ba22168c853655 | https://github.com/CODEJIN/TacoSinger/tree/af58a8f4e8b20e8817990f28a3ba22168c853655 |
ReLUBoundToPOTNet | import torch
from torch.nn import ReLU
from torch.nn import ReLU6
from torch.nn.functional import relu
from torch.nn.functional import relu6
from torch.nn import Conv2d
import torch.nn.functional
class ReLUBoundToPOTNet(torch.nn.Module):
def __init__(self):
super(ReLUBoundToPOTNet, 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 ReLU
fro... | elad-c/model_optimization | ReLUBoundToPOTNet | false | 10,660 | [
"Apache-2.0"
] | 0 | b0ecf41c3f9434008d57d7fe724ff8585e19d4cc | https://github.com/elad-c/model_optimization/tree/b0ecf41c3f9434008d57d7fe724ff8585e19d4cc |
WeightMseLoss | # 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... | yqyao/YOLOv3_Pytorch | WeightMseLoss | false | 16,768 | [
"MIT"
] | 55 | ea392f7d418be94605f86ba2b5d167ec30611def | https://github.com/yqyao/YOLOv3_Pytorch/tree/ea392f7d418be94605f86ba2b5d167ec30611def |
ResBlock | # 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_... | Flemingjp/CDVD-TSP | ResBlock | false | 13,686 | [
"MIT"
] | 232 | a2621476deb9386b1bc02570706f490d582930c8 | https://github.com/Flemingjp/CDVD-TSP/tree/a2621476deb9386b1bc02570706f490d582930c8 |
AttentionBlock | # 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
from torch im... | pomelyu/ML_HW | AttentionBlock | false | 10,710 | [
"MIT"
] | 0 | b87697f3ee86592a34d80c8dbf167a5767731630 | https://github.com/pomelyu/ML_HW/tree/b87697f3ee86592a34d80c8dbf167a5767731630 |
MLP | # 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 ... | GastonMazzei/escher-project-website | MLP | false | 17,291 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
CausalConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | KouheiFurukawa/vq-vae-2-pytorch | CausalConv2d | false | 9,296 | [
"MIT"
] | 0 | ad8a4d8409c2e99e1db790a0e215b346b56b1e1f | https://github.com/KouheiFurukawa/vq-vae-2-pytorch/tree/ad8a4d8409c2e99e1db790a0e215b346b56b1e1f |
ZeroConv2d | # 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
from torch im... | hologerry/glow-pytorch-1 | ZeroConv2d | false | 3,616 | [
"MIT"
] | 0 | 9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 | https://github.com/hologerry/glow-pytorch-1/tree/9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 |
StackTime | import torch
import torch.nn as nn
import torch.utils.data
import torch.jit
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
import torch.utils.data.distributed
class StackTime(nn.Module):
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.jit
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
im... | lamyiowce/training | StackTime | false | 15,872 | [
"Apache-2.0"
] | 567 | da4c959b5a7b65091b850872cdd4014d768c087c | https://github.com/lamyiowce/training/tree/da4c959b5a7b65091b850872cdd4014d768c087c |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from math import sqrt as sqrt
from itertools import product as product
import t... | Kalana304/realtime-action-detection | L2Norm | false | 8,435 | [
"MIT"
] | 26 | a40178c749d60c135290c40a8ac658bac253f0d4 | https://github.com/Kalana304/realtime-action-detection/tree/a40178c749d60c135290c40a8ac658bac253f0d4 |
CodeLoss | import torch
from torch import nn
class CodeLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.MSELoss()
def forward(self, origin_code, trans_code, origin_feature,
trans_feature, weight=0.001):
code_similar = torch.mean(torch.sum((origin_code != trans_code... | 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... | KMU-AELAB/DeepHashing | CodeLoss | false | 9,253 | [
"MIT"
] | 0 | c60069884778246c5a6e11161b78af69e5c8c176 | https://github.com/KMU-AELAB/DeepHashing/tree/c60069884778246c5a6e11161b78af69e5c8c176 |
DataEmbedding_wo_pos | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | thuml/Autoformer | DataEmbedding_wo_pos | false | 16,619 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
bhaModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class bhaModel(nn.Module):
def __init__(self, inShape, outShape):
super().__init__()
self.inShape = inShape
self.outShape = outShape
self.fc1 = nn.Linear(self.inShape, 32)
self.fc2 = nn.Linear(32, 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | BharathC15/bharathML | bhaModel | false | 8,855 | [
"MIT"
] | 0 | ab0460eace3bc83a6b9a7ba7c40e9721baead09a | https://github.com/BharathC15/bharathML/tree/ab0460eace3bc83a6b9a7ba7c40e9721baead09a |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from itertools import chain a... | makarandtapaswi/SlowFast | SE | false | 15,995 | [
"Apache-2.0"
] | 4,914 | 39ef35c9a086443209b458cceaec86a02e27b369 | https://github.com/makarandtapaswi/SlowFast/tree/39ef35c9a086443209b458cceaec86a02e27b369 |
FusedLeakyReLU | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=negative_slop... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | YotamNitzan/pixel2style2pixel | FusedLeakyReLU | false | 2,979 | [
"MIT"
] | 0 | b943f9e6de046a54b901eea1d8714cb02a71605f | https://github.com/YotamNitzan/pixel2style2pixel/tree/b943f9e6de046a54b901eea1d8714cb02a71605f |
RGBBlock | import torch
from torch import nn
import torch.nn.functional as F
class Conv2DMod(nn.Module):
def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1,
dilation=1, **kwargs):
super().__init__()
self.filters = out_chan
self.demod = demod
self.kernel = kernel
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | p0werHu/unet-stylegan2 | RGBBlock | false | 12,865 | [
"MIT"
] | 0 | 9978025e2932d5962fcb724cbd0313b85292f0d3 | https://github.com/p0werHu/unet-stylegan2/tree/9978025e2932d5962fcb724cbd0313b85292f0d3 |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNetwork, self).__init__()
self.log_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | sofya-pugach/spot_mini_mini | PolicyNetwork | false | 16,487 | [
"MIT"
] | 323 | 42770145e91ed2625ccc7e4f4d7016ce14a61464 | https://github.com/sofya-pugach/spot_mini_mini/tree/42770145e91ed2625ccc7e4f4d7016ce14a61464 |
PreNormTransformerDecoderLayer | # 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.... | GeorgeBatch/arch-pre-training | PreNormTransformerDecoderLayer | false | 517 | [
"MIT"
] | 0 | 7ed75868689e9283d61d11360fdbf4e77d4ebd2e | https://github.com/GeorgeBatch/arch-pre-training/tree/7ed75868689e9283d61d11360fdbf4e77d4ebd2e |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
"""
Fully-connected classifier for MNIST.
"""
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | mateuszjurewicz/Copilot | Net | false | 12,778 | [
"MIT"
] | 0 | ccb3eb2755c7cbb5bb035567aa7e73c1d767147a | https://github.com/mateuszjurewicz/Copilot/tree/ccb3eb2755c7cbb5bb035567aa7e73c1d767147a |
DenseBlock | import torch
import torch.nn as nn
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='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
import torch.nn.init as init
assert_size_stride = torch._C... | yzxing87/Invertible-ISP | DenseBlock | false | 16,820 | [
"MIT"
] | 246 | 344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | https://github.com/yzxing87/Invertible-ISP/tree/344dd333dd2a075f6a9e4ffc445dc387ca3014c4 |
CmapPafHeadAttention | # 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.utils.... | Anqi-nus/trtpose | CmapPafHeadAttention | false | 4,912 | [
"MIT"
] | 1 | 723ec95df8b8414b9289af90fbfbc98756792a21 | https://github.com/Anqi-nus/trtpose/tree/723ec95df8b8414b9289af90fbfbc98756792a21 |
SymEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | kevin-kaixu/grass_pytorch | SymEncoder | false | 15,814 | [
"Apache-2.0"
] | 85 | 1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a | https://github.com/kevin-kaixu/grass_pytorch/tree/1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a |
ConvAutoencoder | # 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_... | shankal17/Autoencoders | ConvAutoencoder | false | 4,314 | [
"MIT"
] | 0 | 17aa9f1fe573008fa84694e30e9d395127684191 | https://github.com/shankal17/Autoencoders/tree/17aa9f1fe573008fa84694e30e9d395127684191 |
enhance_net_nopool | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class CSDN_Tem(nn.Module):
def __init__(self, in_ch, out_ch):
super(CSDN_Tem, self).__init__()
self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=3, stride=1, padding=1, g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alisonwqq/Zero-DCE_extension | enhance_net_nopool | false | 14,810 | [
"MIT"
] | 97 | 6b59b36cbe2983e216789583d837bdc88d3e5cf8 | https://github.com/alisonwqq/Zero-DCE_extension/tree/6b59b36cbe2983e216789583d837bdc88d3e5cf8 |
PixelwiseLossL1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | akanametov/pathgan | PixelwiseLossL1 | false | 18,304 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
DiceLoss | # 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... | LisburnLad/open-solution-salt-detection | DiceLoss | false | 777 | [
"MIT"
] | 0 | 9ac292700b2f1351244e29e039425ee706aab92a | https://github.com/LisburnLad/open-solution-salt-detection/tree/9ac292700b2f1351244e29e039425ee706aab92a |
BPRLoss | # 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... | huoxusg/ScenarioMeta | BPRLoss | false | 15,574 | [
"MIT"
] | 79 | ce753da45a3d46ac08961ffc71b2131ae3f7e551 | https://github.com/huoxusg/ScenarioMeta/tree/ce753da45a3d46ac08961ffc71b2131ae3f7e551 |
DepthWiseSeparableConv1d | # 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.jit
import torch.nn
assert_size_stride = torc... | ankmathur96/torchsupport | DepthWiseSeparableConv1d | false | 3,169 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
encoder3 | import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self):
super(encoder3, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
self.reflecPad1 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv2 = nn.Conv2d(3, 64, 3, 1, 0)
self.relu2 = nn.ReLU(inplace=T... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SofiaValdiviesov/LinearStyleTransfer | encoder3 | false | 9,678 | [
"BSD-2-Clause"
] | 0 | 6837c6a9be16bb5981fa0744e5d23f61d08e6940 | https://github.com/SofiaValdiviesov/LinearStyleTransfer/tree/6837c6a9be16bb5981fa0744e5d23f61d08e6940 |
CmapPafHead | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | quantd2/trt_pose | CmapPafHead | false | 16,300 | [
"MIT"
] | 738 | 44c5e826977f20c8dad2d9725313a18cb2189853 | https://github.com/quantd2/trt_pose/tree/44c5e826977f20c8dad2d9725313a18cb2189853 |
PixelShuffleICNR | # 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... | AtlasGooo2/WoodScape | PixelShuffleICNR | false | 13,353 | [
"MIT"
] | 348 | 597d9dda472c09bafea58ea69853948d63197eca | https://github.com/AtlasGooo2/WoodScape/tree/597d9dda472c09bafea58ea69853948d63197eca |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import *
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dime... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Maxi-0902/DRAN | ScaledDotProductAttention | false | 846 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
Critic | import torch
import torch.nn.functional as F
from torch import nn
class Critic(nn.Module):
"""
Value Network (state + action --> value)
"""
def __init__(self, state_size: 'int', action_size: 'int', hidden_size:
'int'=256):
super().__init__()
self.fc1 = 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 import triton_helpers
from torch import nn
assert_s... | jadenvc/puppersim | Critic | false | 10,243 | [
"Apache-2.0"
] | 0 | 1b3f3e3fc0515d5d6101622e0d729c779debfd32 | https://github.com/jadenvc/puppersim/tree/1b3f3e3fc0515d5d6101622e0d729c779debfd32 |
Cnn | import torch
import torch.nn as nn
import torch.nn.functional as F
class Cnn(nn.Module):
def __init__(self):
super(Cnn, self).__init__()
None
self.maxpool = nn.MaxPool2d(2)
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 12, 3, padding=1)
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
import torch.nn as nn
assert_... | satinder147/DeepWay.v2 | Cnn | false | 16,376 | [
"BSD-2-Clause"
] | 57 | c8fca77783ea39f3d17066600d89baf8d0d19a52 | https://github.com/satinder147/DeepWay.v2/tree/c8fca77783ea39f3d17066600d89baf8d0d19a52 |
LeNet5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, (5, 5), padding=0)
self.conv2 = nn.Conv2d(6, 16, (5, 5))
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SuhangGeongyu/2019_SNUST_DEEPLEARNING_HW | LeNet5 | false | 2,962 | [
"MIT"
] | 0 | e6eb119483ab905f558f63341922a56ebee9b5c6 | https://github.com/SuhangGeongyu/2019_SNUST_DEEPLEARNING_HW/tree/e6eb119483ab905f558f63341922a56ebee9b5c6 |
Conditional_Contrastive_loss_plus | import torch
import numpy as np
class Conditional_Contrastive_loss_plus(torch.nn.Module):
def __init__(self, device, batch_size, pos_collected_numerator):
super(Conditional_Contrastive_loss_plus, self).__init__()
self.device = device
self.batch_size = batch_size
self.pos_collected... | 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 nump... | fywang0327/PyTorch-ECGAN | Conditional_Contrastive_loss_plus | false | 12,418 | [
"MIT"
] | 0 | 7c7c8c28c609b1bd2d3aecaeec4bffeb4c9cda6c | https://github.com/fywang0327/PyTorch-ECGAN/tree/7c7c8c28c609b1bd2d3aecaeec4bffeb4c9cda6c |
FactorizationMachine | import torch
import torch.utils.data
class FactorizationMachine(torch.nn.Module):
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
... | 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_... | JazonJiao/pytorch-fm | FactorizationMachine | false | 13,880 | [
"MIT"
] | 734 | 7192e7861fa54341d5b2df995f92858f583ea09e | https://github.com/JazonJiao/pytorch-fm/tree/7192e7861fa54341d5b2df995f92858f583ea09e |
SourceContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LeeeeoLiu/OpenNMT-py | SourceContextGate | false | 2,507 | [
"MIT"
] | 0 | 9be3a8951e9181aabe5440e4ea98173b7e749b5c | https://github.com/LeeeeoLiu/OpenNMT-py/tree/9be3a8951e9181aabe5440e4ea98173b7e749b5c |
AdaAttN | import torch
import torch.utils.data
import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JerryLeolfl/AdaAttN | AdaAttN | false | 2,412 | [
"MIT"
] | 0 | 062c66f7818b344e3730ce9d6df7af03f9acb4f5 | https://github.com/JerryLeolfl/AdaAttN/tree/062c66f7818b344e3730ce9d6df7af03f9acb4f5 |
ChannelSELayer3D | # 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_... | YilinLiu97/AmygNet-Pytorch | ChannelSELayer3D | false | 18,141 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
FNormTest | import torch
import torch.nn as nn
class FNormTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FNormTest, self).__init__()
def forward(self, x):
x = torch.norm(x, p=2, dim=[1, 2])
return x
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | goldbattle/onnx2keras | FNormTest | false | 12,452 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
MedianPool2d | # 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.nn.modules.utils import _pair
import torch.nn as nn
import tor... | Fanzhongjie/ARFE | MedianPool2d | false | 444 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v = torch.var(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
m = torch.mean(w, dim=[1, 2, 3], keepdim=True)
w = (w - m) / torch.sqrt(v + 1e-10)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | hrlblab/Glo-In-One | StdConv2d | false | 6,818 | [
"Apache-2.0"
] | 1 | 7daef49c557bccd6f5c956b88603357346dc78a2 | https://github.com/hrlblab/Glo-In-One/tree/7daef49c557bccd6f5c956b88603357346dc78a2 |
TinyConvNet2d | # 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... | Tomaz-Vieira/tiktorch | TinyConvNet2d | false | 18,016 | [
"MIT"
] | 8 | 2d6803c4ba5e26e4b27bf8af6638040fa4fc5628 | https://github.com/Tomaz-Vieira/tiktorch/tree/2d6803c4ba5e26e4b27bf8af6638040fa4fc5628 |
Block | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KKallidromitis/vissl | Block | false | 5,454 | [
"MIT"
] | 1 | c553e7f6b13c5fa951e3f989beb129899eb8cc80 | https://github.com/KKallidromitis/vissl/tree/c553e7f6b13c5fa951e3f989beb129899eb8cc80 |
DirichletPolicyTwoLayer | import torch
import numpy as np
import torch.nn.functional as F
import torch.distributions as td
import torch.nn as nn
class PolicyNetwork(nn.Module):
"""Base class for stochastic policy networks."""
def __init__(self):
super().__init__()
def forward(self, state):
"""Take state as input,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | wessle/costaware | DirichletPolicyTwoLayer | false | 10,997 | [
"MIT"
] | 0 | 151502308411528eaa703d353d138fc809e59d8e | https://github.com/wessle/costaware/tree/151502308411528eaa703d353d138fc809e59d8e |
PermEqui2_mean | import torch
from torch import nn
class PermEqui2_mean(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.Gamma = nn.Linear(in_dim, out_dim)
self.Lambda = nn.Linear(in_dim, out_dim, bias=False)
self.weight = self.Gamma.weight
self.bias = self.Gamma.bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | ydiller/NoMoreNMS | PermEqui2_mean | false | 4,610 | [
"Apache-2.0"
] | 0 | 1c1557357e5312c287f0971c840060deb1bcd039 | https://github.com/ydiller/NoMoreNMS/tree/1c1557357e5312c287f0971c840060deb1bcd039 |
SimpleLogSoftmaxModel | # 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.jit
impor... | briancoutinho/glow | SimpleLogSoftmaxModel | false | 12,576 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
HSwish | import torch
from torch import nn
import torch.nn.functional as F
class HSwish(nn.Module):
def forward(self, x):
out = x * F.relu6(x + 3, inplace=True) / 6
return out
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._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | LDOUBLEV/DBNet.pytorch | HSwish | false | 9,417 | [
"Apache-2.0"
] | 0 | 206f4a1e5cc3686284476f029a26fc69f610e898 | https://github.com/LDOUBLEV/DBNet.pytorch/tree/206f4a1e5cc3686284476f029a26fc69f610e898 |
EncoderImagePrecomp | # 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 numpy as np
... | KunpengLi1994/VSRN | EncoderImagePrecomp | false | 13,962 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
myNet | # 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_... | daxiongpro/pytorch-tutorial | myNet | false | 1,808 | [
"MIT"
] | 0 | abafc32f7ee1092024085f703e4ced51ce358a1b | https://github.com/daxiongpro/pytorch-tutorial/tree/abafc32f7ee1092024085f703e4ced51ce358a1b |
ScaleNetwork | # 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.... | chawins/adv-exp | ScaleNetwork | false | 6,441 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
HardMish | import torch
from torch import nn
import torch.cuda
def hard_mish(x, inplace: 'bool'=False):
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMish(nn.Module):
"""
Hard Mish
Experimental, based on notes by Mi... | 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.cuda
assert_size_stride = torch._C._dynamo.guards.asser... | LoveEachDay/towhee | HardMish | false | 11,652 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
OhemCELoss2D | # 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 math
import tor... | MarcosPampuch/TDNet_CARLA | OhemCELoss2D | false | 819 | [
"MIT"
] | 0 | efc1c872966f1cef49b82723170586a6abcfb524 | https://github.com/MarcosPampuch/TDNet_CARLA/tree/efc1c872966f1cef49b82723170586a6abcfb524 |
RelativeMSE | # 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 as th
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | PeterZs/sbmc | RelativeMSE | false | 5,710 | [
"Apache-2.0"
] | 1 | ac3f5452efe0166ea73942f37cc60b1f0e1ee555 | https://github.com/PeterZs/sbmc/tree/ac3f5452efe0166ea73942f37cc60b1f0e1ee555 |
ResidualBlock | # 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.... | joaquingv12/Solving-Image-Processing-Problems-with-Python-Part1 | ResidualBlock | false | 6,971 | [
"MIT"
] | 1 | 42512672d1dc660dabc2d4570e891315f5264b12 | https://github.com/joaquingv12/Solving-Image-Processing-Problems-with-Python-Part1/tree/42512672d1dc660dabc2d4570e891315f5264b12 |
BertSelfAttention | # 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.... | Hzfinfdu/Black-Box-Tuning | BertSelfAttention | false | 4,738 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
_GatedLinearUnit | # 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... | amadejkocbek/darts | _GatedLinearUnit | false | 12,109 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
GatedConv | import torch
import torch.nn as nn
import torch.utils.data
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,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | musyoku/ffjord | GatedConv | false | 7,302 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
TwoLayerNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | KentonMurray/ProxGradPytorch | TwoLayerNet | false | 8,431 | [
"MIT"
] | 27 | c534a49142ac9ec149ca67de24bb0487fde1607b | https://github.com/KentonMurray/ProxGradPytorch/tree/c534a49142ac9ec149ca67de24bb0487fde1607b |
PARALossSoftmax | # 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
... | igorvlnascimento/open-nre | PARALossSoftmax | false | 12,527 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
CEL | # 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... | Farzanehkaji/MINet | CEL | false | 17,265 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 |
CosNorm_Classifier | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class CosNorm_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(CosNorm_Classifier, self).__init__()
self.in_dims = in_dims
self.out_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | PiperLiu/AliProducts | CosNorm_Classifier | false | 5,719 | [
"MIT"
] | 1 | f51884c4dae035a879dbaca2c1575797f30ee7d3 | https://github.com/PiperLiu/AliProducts/tree/f51884c4dae035a879dbaca2c1575797f30ee7d3 |
MatrixArgMax | import torch
import torch.nn as nn
import torch.autograd
class MatrixArgMax(nn.Module):
def __init__(self):
super(MatrixArgMax, self).__init__()
def forward(self, x):
z = torch.argmax(x)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixArgMax | false | 14,341 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
BertSelfOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Splendon/examples | BertSelfOutput | false | 3,377 | [
"MIT"
] | 0 | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | https://github.com/Splendon/examples/tree/ed4a8a01857b6ddca49559141acf5d0986eb01e1 |
OneDilate | import torch
import torch.nn as nn
import torch.nn.functional as F
class OneDilate(nn.Module):
def __init__(self, kernel_size=10, channels=3, gpu=True):
super(OneDilate, self).__init__()
self.kernel_size = kernel_size
self.channels = channels
gaussian_kernel = torch.ones(1, 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... | williamyang1991/DeepPS | OneDilate | false | 11,093 | [
"MIT"
] | 0 | f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 | https://github.com/williamyang1991/DeepPS/tree/f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 |
MaskedLanguageModel | # 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.... | SivilTaram/dialogue-utterance-rewriter-pytorch | MaskedLanguageModel | false | 2,923 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | lykasbongbongbong/Pytorch | Net | false | 10,434 | [
"MIT"
] | 0 | f01d89fb51ac939f5a110f5ab6190c11917e66fc | https://github.com/lykasbongbongbong/Pytorch/tree/f01d89fb51ac939f5a110f5ab6190c11917e66fc |
RegLoss | import torch
import torch.nn as nn
def _reg_loss(regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects)
"""
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr).float()
... | 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
... | SaqibMamoon/GSDT | RegLoss | false | 5,803 | [
"MIT"
] | 1 | e11c52a67291e973016ed34c3c95659e0af32d48 | https://github.com/SaqibMamoon/GSDT/tree/e11c52a67291e973016ed34c3c95659e0af32d48 |
BCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
def binary_cross_entropy(inputs, target, weight=None, reduction='mean',
smooth_eps=None, from_logits=False):
"""cross entropy loss, with support for label smoothing https://arxiv.org/abs/1512.00567"""
smooth_eps = smooth... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | cwlacewe/SNAS-Series | BCELoss | false | 15,092 | [
"MIT"
] | 133 | 92ac8031f718235aecaefb9967851f8f355dbca0 | https://github.com/cwlacewe/SNAS-Series/tree/92ac8031f718235aecaefb9967851f8f355dbca0 |
IPDFeature | # 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... | oucxlw/ConferencingSpeech2021 | IPDFeature | false | 16,205 | [
"Apache-2.0"
] | 98 | 617df8116c0510b2addadb1de374d7b50eea4f2b | https://github.com/oucxlw/ConferencingSpeech2021/tree/617df8116c0510b2addadb1de374d7b50eea4f2b |
MCDropout2d | import torch
from torch import Tensor
import torch.nn as nn
from torch.functional import F
import torch.nn.functional as F
class MCDropout2d(nn.Dropout2d):
"""2D dropout that stays on during training and testing
"""
def forward(self, input: 'Tensor') ->Tensor:
return F.dropout2d(input, self.p, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jiwoncpark/ex-con | MCDropout2d | false | 6,949 | [
"MIT"
] | 1 | 6775d11ec1c3e7005890e58d16dd07b711861cdf | https://github.com/jiwoncpark/ex-con/tree/6775d11ec1c3e7005890e58d16dd07b711861cdf |
_AddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torc... | amadejkocbek/darts | _AddNorm | false | 12,105 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
QREmbeddingBag | import torch
import numpy as np
from torch import nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one
using the quotient of the indices and the other using the remainder
of the indices, 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 numpy as np
from torch import nn
from torch.nn.parameter import Paramete... | divyanshugit/EnvisEdge | QREmbeddingBag | false | 3,480 | [
"Apache-2.0"
] | 0 | 26b21fd0eb665fa23a8b8a825c9bf460994d6714 | https://github.com/divyanshugit/EnvisEdge/tree/26b21fd0eb665fa23a8b8a825c9bf460994d6714 |
SimpleCNN | import torch
import torch.nn.functional as F
class SimpleCNN(torch.nn.Module):
def __init__(self, in_ch=1, out_ch=3):
super(SimpleCNN, self).__init__()
self.conv1 = torch.nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1,
padding=1)
self.conv2 = torch.nn.Conv2d(out_ch, out_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Arjun-Arora/CS348B_project | SimpleCNN | false | 4,873 | [
"BSD-2-Clause"
] | 1 | 000ced8edbc3554db74db36ebcd76042d17398ee | https://github.com/Arjun-Arora/CS348B_project/tree/000ced8edbc3554db74db36ebcd76042d17398ee |
MultiRelu | import torch
from torch import Tensor
from typing import Tuple
import torch.nn as nn
from typing import no_type_check
class MultiRelu(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.relu1 = nn.ReLU(inplace=inplace)
self.relu2 = nn.ReLU(inplace=inplace... | 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... | aravipati12/captum | MultiRelu | false | 10,115 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
MAB | import math
import torch
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
class MAB(torch.nn.Module):
def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
'int', Conv: 'Optio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ClintvanHoesel/MXMNet_adapted | MAB | false | 334 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
BertPredictionHeadTransform | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | kimihitosugiyama/text_analysis | BertPredictionHeadTransform | false | 3,840 | [
"Apache-2.0"
] | 0 | 8f51022957928c31e52af1e0fd407daca3addb40 | https://github.com/kimihitosugiyama/text_analysis/tree/8f51022957928c31e52af1e0fd407daca3addb40 |
GeneralizedFocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | ZhuokunYao/smoke | GeneralizedFocalLoss | false | 1,323 | [
"MIT"
] | 0 | d524fbe43b1aba6078c25d9aca7924b71a635e1d | https://github.com/ZhuokunYao/smoke/tree/d524fbe43b1aba6078c25d9aca7924b71a635e1d |
ResolutionScalingLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CV-IP/interfacegan | ResolutionScalingLayer | false | 13,458 | [
"MIT"
] | 855 | 5a556b8e693f6e1888f769f653aaafaaccca5dc2 | https://github.com/CV-IP/interfacegan/tree/5a556b8e693f6e1888f769f653aaafaaccca5dc2 |
ConvBlock | import torch
import torch.nn as nn
import torch.utils.data
class WSConv2d(nn.Module):
"""
Weight scaled Conv2d (Equalized Learning Rate)
Note that input is multiplied rather than changing weights
this will have the same result.
Inspired by:
https://github.com/nvnbny/progressive_growing_of_gan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | SongsLearning/Machine-Learning-Collection | ConvBlock | false | 1,084 | [
"MIT"
] | 0 | a8dff83969f67d37f70a89db06b851057d2da539 | https://github.com/SongsLearning/Machine-Learning-Collection/tree/a8dff83969f67d37f70a89db06b851057d2da539 |
PixelSort | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | pshn111/803-Project | PixelSort | false | 12,903 | [
"MIT"
] | 0 | 19430f25d91b31e4b9a7f1d864e2aa2851dcddf0 | https://github.com/pshn111/803-Project/tree/19430f25d91b31e4b9a7f1d864e2aa2851dcddf0 |
CatKLLoss | import torch
from torch.nn.modules.loss import _Loss
class CatKLLoss(_Loss):
def __init__(self, reduction='none'):
super(CatKLLoss, self).__init__()
assert reduction in ['none', 'sum', 'mean']
self.reduction = reduction
def forward(self, log_qy, log_py):
"""
KL(qy|py)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | imguozhen/proactive-chat | CatKLLoss | false | 10,289 | [
"Apache-2.0"
] | 0 | 80d13e28cb93c26a65ace0a028c53fd0bafcdbf9 | https://github.com/imguozhen/proactive-chat/tree/80d13e28cb93c26a65ace0a028c53fd0bafcdbf9 |
LocalDiscriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class LocalDiscriminator(nn.Module):
"""The local discriminator class.
A network that analyses the relation between the
output of the encoder y, and the feature map M.
It is called "local" because it compares y with... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | LocalDiscriminator | false | 16,172 | [
"MIT"
] | 130 | 6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 | https://github.com/neuralsyn/self-supervised-relational-reasoning/tree/6ecfafcf4a36c2eacef7ddd5bd1b23c28fbb14c8 |
EncoderSlot | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | CatarauCorina/representation_learning | EncoderSlot | false | 8,917 | [
"Apache-2.0"
] | 0 | bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 | https://github.com/CatarauCorina/representation_learning/tree/bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 |
StyleAdaptiveLayerNorm | import torch
import torch.nn
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class AffineLinear(nn.Module):
def __init__(self, in_dim, out_dim):
super(AffineLinear, self).__init__()
affine = nn.Linear(in_dim, out_dim)
self.affine = affine
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fro... | DanielLin94144/StyleSpeech | StyleAdaptiveLayerNorm | false | 5,056 | [
"MIT"
] | 1 | 809e8ead55bea2c63f714fdc19bf24d80f0f546c | https://github.com/DanielLin94144/StyleSpeech/tree/809e8ead55bea2c63f714fdc19bf24d80f0f546c |
ScaleDotProductAttention | # 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.... | bsgiovanini/transformer | ScaleDotProductAttention | false | 1,584 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
AdaIN | # 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 ... | Noodles-321/RegistrationEval | AdaIN | false | 8,649 | [
"MIT"
] | 38 | 3631d3d5bd65acf980fcfed803fa6125970f3e88 | https://github.com/Noodles-321/RegistrationEval/tree/3631d3d5bd65acf980fcfed803fa6125970f3e88 |
LocallyConnectedLayer1d | # 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | adityayedetore/LCRNN | LocallyConnectedLayer1d | false | 3,033 | [
"MIT"
] | 0 | 7b6afaf6098fed584b90fe0196cfd26aa6a190c5 | https://github.com/adityayedetore/LCRNN/tree/7b6afaf6098fed584b90fe0196cfd26aa6a190c5 |
PrimaryCaps | # 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 ... | spikefairway/CapsNet-PyTorch | PrimaryCaps | false | 4,381 | [
"MIT"
] | 0 | 76aaabaad01283333a5f73a564cb1461449b4449 | https://github.com/spikefairway/CapsNet-PyTorch/tree/76aaabaad01283333a5f73a564cb1461449b4449 |
MMD | import torch
from torch import nn
class MMD(nn.Module):
def __init__(self):
super().__init__()
def _guassian_kernel(self, source, target, kernel_mul=2.0, kernel_num=5,
fix_sigma=None):
n_samples = int(source.size()[0]) + int(target.size()[0])
total = torch.cat([source, target... | 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_... | BetterRaven/Transfer-Learning_vscode | MMD | false | 4,913 | [
"MIT"
] | 1 | 90c9bce630f54fd2322cce8fab5fe1d074ff141c | https://github.com/BetterRaven/Transfer-Learning_vscode/tree/90c9bce630f54fd2322cce8fab5fe1d074ff141c |
ResidualBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.channels = channels
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | crisnyoung/awesome-DeepLearning | ResidualBlock | false | 3,363 | [
"Apache-2.0"
] | 0 | 0f4d0e8cc6f6c662c9a058d4af7610bf1d2a947d | https://github.com/crisnyoung/awesome-DeepLearning/tree/0f4d0e8cc6f6c662c9a058d4af7610bf1d2a947d |
LipschitzCube | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | hologerry/residual-flows | LipschitzCube | false | 10,298 | [
"MIT"
] | 0 | 33a3639150490279c2e13238dd6244b80c52adf7 | https://github.com/hologerry/residual-flows/tree/33a3639150490279c2e13238dd6244b80c52adf7 |
ActNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parame... | lingzenan/invertible-resnet | ActNorm | false | 7,101 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
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
from torch.autograd import Function
import math
import torch.nn as nn
from torch... | kampta/multiview-shapes | ToRGB | false | 3,799 | [
"MIT"
] | 0 | a79eb4b492be8c2c279e2c69b13d5a19dff1621b | https://github.com/kampta/multiview-shapes/tree/a79eb4b492be8c2c279e2c69b13d5a19dff1621b |
SwiGLU | import torch
import torch.nn as nn
class PositionWiseFeedForward(nn.Module):
"""
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | robburdon/pytorch_tabular | SwiGLU | false | 16,325 | [
"MIT"
] | 560 | 9bf75f22c6e1b3033ad699713e77c283d55f3555 | https://github.com/robburdon/pytorch_tabular/tree/9bf75f22c6e1b3033ad699713e77c283d55f3555 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | srivarshan-s/Neural-Chess-2D | Net | false | 4,411 | [
"MIT"
] | 0 | 81ec7eb9b4c3c82dc7f6ba5bd4313bd6ede9994e | https://github.com/srivarshan-s/Neural-Chess-2D/tree/81ec7eb9b4c3c82dc7f6ba5bd4313bd6ede9994e |
Linear | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | CFF-Dream/pytorch_geometric | Linear | false | 2,047 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
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