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 |
|---|---|---|---|---|---|---|---|---|---|---|
CausalConv1d | import torch
import torch.nn as nn
import torch.utils.data
import torch
class CausalConv1d(nn.Module):
"""A 1D causal convolution layer.
Input: (B, D_in, T), where B is the minibatch size, D_in is the number of
dimensions per step, and T is the number of steps.
Output: (B, D_out, T), where B is 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
import torch.nn as nn
import torch.utils.data
import torch
assert_size_stride = ... | sagelywizard/snail | CausalConv1d | false | 16,350 | [
"MIT"
] | 100 | 1c64787aa970c82f65c3c9d253531d1c2b1bee08 | https://github.com/sagelywizard/snail/tree/1c64787aa970c82f65c3c9d253531d1c2b1bee08 |
SpatialAttentionGate | import torch
import torch.nn.functional as F
import torch.nn as nn
class SpatialAttentionGate(nn.Module):
def __init__(self, channel, reduction=16):
super(SpatialAttentionGate, self).__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
self.fc2 = nn.Conv2d(reducti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | lawwu/nni | SpatialAttentionGate | false | 10,606 | [
"MIT"
] | 0 | b869dd48dfe36392e7b78c70ea35eb6d4b4779dc | https://github.com/lawwu/nni/tree/b869dd48dfe36392e7b78c70ea35eb6d4b4779dc |
LeakyReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | altescy/xtorch | LeakyReLU | false | 9,708 | [
"MIT"
] | 0 | bcbbbe645f4d62c211af5b3555c526cc60792c32 | https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32 |
ShakeResNeXt | # 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 math
from torch.nn imp... | ang421/dda | ShakeResNeXt | false | 9,913 | [
"MIT"
] | 0 | 391ad696ec8479ce41a0d7d6bfbfae06edaddf67 | https://github.com/ang421/dda/tree/391ad696ec8479ce41a0d7d6bfbfae06edaddf67 |
Block | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CarnoZhao/mmsegmentation | Block | false | 7,855 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
SinusoidPositionalEmbedding | import torch
import torch.nn as nn
class SinusoidPositionalEmbedding(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_model)
pos[:, 0::2], pos[:, 1::2] = pos[... | 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... | yzhangcs/parser | SinusoidPositionalEmbedding | false | 16,789 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a |
NN_softmax | import torch
from torch import nn
import torch.nn.functional as F
class NN_logsoftmax(nn.Module):
"""Build a new class for the network you want to run, returning log
softmax"""
def set_parameters(self, initializers):
"""Set the parameter values obtained from vanilla NN as initializers"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | laravomfell/tvd_loss | NN_softmax | false | 7,076 | [
"MIT"
] | 1 | b30a925f95985a03ff70bfa40a6ec3662432779d | https://github.com/laravomfell/tvd_loss/tree/b30a925f95985a03ff70bfa40a6ec3662432779d |
GlobalMaxPooling | # 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... | numb3r33/toxic_comments_classification | GlobalMaxPooling | false | 10,596 | [
"MIT"
] | 0 | c5de56751aee29b6dee6e330237a4fd0bcd7fd51 | https://github.com/numb3r33/toxic_comments_classification/tree/c5de56751aee29b6dee6e330237a4fd0bcd7fd51 |
TransformerEncoderLayer | # 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.... | FrankVerhoef/Persona-Dialogue-Generation | TransformerEncoderLayer | false | 5,186 | [
"MIT"
] | 1 | ffd8413c2e8b6446097902dd1c496aeb24b852b4 | https://github.com/FrankVerhoef/Persona-Dialogue-Generation/tree/ffd8413c2e8b6446097902dd1c496aeb24b852b4 |
TransformerEncoderLayer | # 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.... | marcinwitkowski/icefall | TransformerEncoderLayer | false | 10,499 | [
"Apache-2.0"
] | 0 | 73e917f689fa2ebfcfe5d484a34a262e74b77581 | https://github.com/marcinwitkowski/icefall/tree/73e917f689fa2ebfcfe5d484a34a262e74b77581 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
self.b = nn.Parame... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | NickSchoelkopf/SummerTime | LayerNorm | false | 891 | [
"Apache-2.0"
] | 0 | 9a89aab8e1544e3c52c043b9c47ab325e665e11e | https://github.com/NickSchoelkopf/SummerTime/tree/9a89aab8e1544e3c52c043b9c47ab325e665e11e |
GlobalPooling2D | import torch
from torch import nn
from typing import *
class GlobalPooling2D(nn.Module):
def __init__(self):
super(GlobalPooling2D, self).__init__()
def forward(self, x):
x = x.view(x.size(0), x.size(1), -1)
x = torch.mean(x, 2)
x = x.view(x.size(0), -1)
return x
de... | 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 typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | HughMun/MultiBench | GlobalPooling2D | false | 13,797 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
ComplexConvTranspose2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import ConvTranspose2d
assert_size_str... | drydenwiebe/complexPyTorch | ComplexConvTranspose2d | false | 12,328 | [
"MIT"
] | 0 | cea88ba7ee5692dfa1b40f0ba609ef14160d5073 | https://github.com/drydenwiebe/complexPyTorch/tree/cea88ba7ee5692dfa1b40f0ba609ef14160d5073 |
FeatureMatchingLoss | # 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.utils.data
import torch
from torch import nn
assert_size_str... | alexander-telepov/RGB2MSI | FeatureMatchingLoss | false | 6,164 | [
"BSD-3-Clause"
] | 1 | 99f81f5547d40d0c92cfde39994a8c53629bd0f7 | https://github.com/alexander-telepov/RGB2MSI/tree/99f81f5547d40d0c92cfde39994a8c53629bd0f7 |
ScaledDotProductAttention | import torch
import torch.optim.lr_scheduler
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, d_model, attention_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temper = d_model ** 0.5
self.dropout = nn.Dropout(attention_dropout)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | interrogator/self-attentive-parser | ScaledDotProductAttention | false | 15,593 | [
"MIT"
] | 88 | 660d0161cb6ec6455d1525d029ff09362dcf7faf | https://github.com/interrogator/self-attentive-parser/tree/660d0161cb6ec6455d1525d029ff09362dcf7faf |
BoundNot | from _paritybench_helpers import _mock_config
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation:
def __init__(self):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundNot | false | 7,281 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
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 import triton_helpers
import torch.nn as nn
assert_... | f2010126/DL_Labs | MLP | false | 3,484 | [
"BSD-3-Clause"
] | 0 | ee81d8aa6027846fc32c98feb9079211c59aa0e9 | https://github.com/f2010126/DL_Labs/tree/ee81d8aa6027846fc32c98feb9079211c59aa0e9 |
DotProd | # 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... | mrernst/rl_robotics_research | DotProd | false | 10,612 | [
"MIT"
] | 0 | 0bc446cfb69591cb4ee3ce8d39815c463090a5f6 | https://github.com/mrernst/rl_robotics_research/tree/0bc446cfb69591cb4ee3ce8d39815c463090a5f6 |
ViTStemPatchify | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn as nn
import torch.utils.data
assert... | MAC-AutoML/XCompression | ViTStemPatchify | false | 5,567 | [
"MIT"
] | 1 | 9f76eb3ccfb3057110ecf12aa48dec00a4667a25 | https://github.com/MAC-AutoML/XCompression/tree/9f76eb3ccfb3057110ecf12aa48dec00a4667a25 |
BertPooler | # 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... | Adoni/pytorch-pretrained-BERT | BertPooler | false | 3,485 | [
"Apache-2.0"
] | 0 | 845c33f00e933626dcfc96e0923ecf034295ef75 | https://github.com/Adoni/pytorch-pretrained-BERT/tree/845c33f00e933626dcfc96e0923ecf034295ef75 |
DilatedBasicBlock | # 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.... | Galaxies99/alpha-protein | DilatedBasicBlock | false | 17,319 | [
"MIT"
] | 4 | db4b77ab48d5905ade5d4a66004f8387773718fa | https://github.com/Galaxies99/alpha-protein/tree/db4b77ab48d5905ade5d4a66004f8387773718fa |
GumbelSoftmax | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional as F
class GumbelSoftmax(nn.Module):
def __init__(self, f_dim, c_dim):
super(GumbelSoftmax, self).__init__()
self.logits = nn.Linear(f_dim, c_dim)
self.f_dim = f_dim
self.c_dim = c_dim
d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Kaya176/GMVAE | GumbelSoftmax | false | 9,252 | [
"MIT"
] | 0 | 6369be52dbac796e2f836f51b16aaa5c61247350 | https://github.com/Kaya176/GMVAE/tree/6369be52dbac796e2f836f51b16aaa5c61247350 |
CausalConv2d | import torch
import torch.utils.data
import torch
from torch import nn
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 |
ResidualBlock | import torch
import torch.utils.data
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, hidden, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=hidden,
kernel_size=3, stride=1, padding=1)
self.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | YigitGunduc/self-driving-car | ResidualBlock | false | 2,978 | [
"MIT"
] | 0 | 2be31f6473c911cf004236ce0874cb2da8fe8ad1 | https://github.com/YigitGunduc/self-driving-car/tree/2be31f6473c911cf004236ce0874cb2da8fe8ad1 |
BiInteractionPooling | # 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
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Fanxingye/DeepRS | BiInteractionPooling | false | 13,836 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d |
DWT | # 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | directgroup/direct | DWT | false | 15,187 | [
"Apache-2.0"
] | 55 | 78cdd530b3c93e31c11d8963880e6329f0989243 | https://github.com/directgroup/direct/tree/78cdd530b3c93e31c11d8963880e6329f0989243 |
RelativeThreshold_RegLoss | # 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
import torch.nn.init
assert_size_stride = torch._C.... | andry900/NN-Project | RelativeThreshold_RegLoss | false | 1,453 | [
"MIT"
] | 0 | e04a83029f5990d9b65216ab0648a8826a8ebca7 | https://github.com/andry900/NN-Project/tree/e04a83029f5990d9b65216ab0648a8826a8ebca7 |
Beta2 | import torch
import numpy as np
import torch.nn as nn
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Beta2(nn.Module):
def __init__(self, action_dim, init_std=0.25, learn_std=False):
super(Beta2, self).__init__()
asser... | 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | RohanPankaj/apex | Beta2 | false | 998 | [
"MIT"
] | 0 | 74e96386bf9446d1179106d6d65ea0368c1b5b27 | https://github.com/RohanPankaj/apex/tree/74e96386bf9446d1179106d6d65ea0368c1b5b27 |
ConvLayer | # 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 math as tl_math
from typing import *
i... | code-backdoor/code-backdoor | ConvLayer | false | 15,055 | [
"MIT"
] | 71 | 1eeb3d79aa8a54c8f08e8d0156b569de5edd974e | https://github.com/code-backdoor/code-backdoor/tree/1eeb3d79aa8a54c8f08e8d0156b569de5edd974e |
SeqFC1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqFC1(nn.Module):
""" Neural network definition
"""
def __init__(self, size):
super(SeqFC1, self).__init__()
self.size = size
self.fc1 = nn.Linear(in_features=self.size, out_features=16)
self.fc2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Thibaud-Ardoin/Dial-a-Ride | SeqFC1 | false | 5,873 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
DQN_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 import triton_helpers
import torch.nn as nn
import ... | CoAxLab/azad | DQN_mlp | false | 17,194 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
MultiheadAttention | import math
import torch
import torch.nn as nn
import torch as t
class MultiheadAttention(nn.Module):
"""
Multihead attention mechanism (dot attention)
"""
def __init__(self, num_hidden_k):
"""
:param num_hidden_k: dimension of hidden
"""
super(MultiheadAttention, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Munna-Manoj/Team7_TTS | MultiheadAttention | false | 11,733 | [
"MIT"
] | 0 | 5e2d473a2afe429023876bcc51c2ac966a4938b8 | https://github.com/Munna-Manoj/Team7_TTS/tree/5e2d473a2afe429023876bcc51c2ac966a4938b8 |
SVM | # 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... | XIAOYEJIAYOU/GSAN | SVM | false | 18,076 | [
"MIT"
] | 6 | 8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | https://github.com/XIAOYEJIAYOU/GSAN/tree/8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 |
GroupedLinearLayer | import torch
from torch import nn
import torch.utils.checkpoint
class GroupedLinearLayer(nn.Module):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C... | Clemens123/transformers | GroupedLinearLayer | false | 11,498 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Policy | # 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.... | caimingxue/Reinforcement-Learning | Policy | false | 6,376 | [
"MIT"
] | 1 | 5ccb8a6a25b41526f4d6195e69964245abc46d38 | https://github.com/caimingxue/Reinforcement-Learning/tree/5ccb8a6a25b41526f4d6195e69964245abc46d38 |
MultiHeadAttention | # 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.... | Hcnaeg/DI-engine | MultiHeadAttention | false | 2,402 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
MAB | # 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.... | OpenXAIProject/dac | MAB | false | 8,647 | [
"MIT"
] | 17 | 652776e21b56dcb68839363bb077d5c5ea28d81e | https://github.com/OpenXAIProject/dac/tree/652776e21b56dcb68839363bb077d5c5ea28d81e |
UpBlock | # 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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | UpBlock | false | 9,561 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
Scaled_Dot_Product_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tianjiansmile/Chinese-Text-Classification-Pytorch | Scaled_Dot_Product_Attention | false | 10,875 | [
"MIT"
] | 0 | 05cc211b161f61e6bb32ab185dadcffec2f5b5de | https://github.com/tianjiansmile/Chinese-Text-Classification-Pytorch/tree/05cc211b161f61e6bb32ab185dadcffec2f5b5de |
ZeroCenter | # 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
@triton.jit
def triton_per_fused_mean_sub_0(in_ptr0,... | vinnamkim/segmentation_models.pytorch | ZeroCenter | false | 4,486 | [
"MIT"
] | 0 | f967ded34df6fb536e8e8cba9b6491ae63b939f5 | https://github.com/vinnamkim/segmentation_models.pytorch/tree/f967ded34df6fb536e8e8cba9b6491ae63b939f5 |
VAE | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, img_channels, latent_size):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.img_channels = img_channels
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | susanwe/world-models | VAE | false | 10,907 | [
"MIT"
] | 0 | 0f246a430683e6ab741726df0a97f35830044356 | https://github.com/susanwe/world-models/tree/0f246a430683e6ab741726df0a97f35830044356 |
Scaled_Dot_Product_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.... | Ch4ndelier/Transformer_Zero_Velocity_classification | Scaled_Dot_Product_Attention | false | 17,076 | [
"MIT"
] | 6 | 857efb66189c503e983c11bd7dde16ad19c51ada | https://github.com/Ch4ndelier/Transformer_Zero_Velocity_classification/tree/857efb66189c503e983c11bd7dde16ad19c51ada |
PolicyNet | # 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_... | Alfo5123/ConcreteDropout | PolicyNet | false | 16,878 | [
"MIT"
] | 7 | c442871553e20a2de078c0fbac7fa52302d50abf | https://github.com/Alfo5123/ConcreteDropout/tree/c442871553e20a2de078c0fbac7fa52302d50abf |
M1Criterion | import torch
import torch.nn as nn
import torch.nn.functional as F
class M1Criterion(nn.Module):
def __init__(self, x_sigma=1, bce_reconstruction=True):
super(M1Criterion, self).__init__()
self.x_sigma = x_sigma
self.bce_reconstruction = bce_reconstruction
def forward(self, x, x_reco... | 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... | PaperCodeSubmission/ICML2020-697 | M1Criterion | false | 8,658 | [
"MIT"
] | 12 | 00f7732c236b9c6234e76a47dfebe5de314d5c01 | https://github.com/PaperCodeSubmission/ICML2020-697/tree/00f7732c236b9c6234e76a47dfebe5de314d5c01 |
Rot180 | import torch
import torch.nn as nn
def rot180(input: 'torch.Tensor') ->torch.Tensor:
"""Rotate a tensor image or a batch of tensor images
180 degrees. Input must be a tensor of shape (C, H, W)
or a batch of tensors :math:`(*, C, H, W)`.
Args:
input (torch.Tensor): input tensor
Returns:
... | 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... | IEM-Computer-Vision/kornia | Rot180 | false | 9,255 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | f98bd9a2158a6e59cda076d55d476acf13f4e0af | https://github.com/IEM-Computer-Vision/kornia/tree/f98bd9a2158a6e59cda076d55d476acf13f4e0af |
ConfidentMSELoss | from torch.nn import Module
import torch
class ConfidentMSELoss(Module):
def __init__(self, threshold=0.96):
self.threshold = threshold
super().__init__()
def forward(self, input, target):
n = input.size(0)
conf_mask = torch.gt(target, self.threshold).float()
input_fl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | neuropoly/medicaltorch | ConfidentMSELoss | false | 7,327 | [
"Apache-2.0"
] | 1 | ac129fe894cb1906285dfe380ba4f0aa3bdec787 | https://github.com/neuropoly/medicaltorch/tree/ac129fe894cb1906285dfe380ba4f0aa3bdec787 |
rec_attention | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def batch_product(iput, mat2):
result = None
for i in range(iput.size()[0]):
op = torch.mm(iput[i], mat2)
op = op.unsqueeze(0)
if result is None:
result = op
else:
result = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Luma-1994/lama | rec_attention | false | 14,405 | [
"MIT"
] | 137 | 60d802e2e4cce789f03eea11b038212ba5f7fd1b | https://github.com/Luma-1994/lama/tree/60d802e2e4cce789f03eea11b038212ba5f7fd1b |
Conv3x3 | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | aliasghar53/packnet-sfm | Conv3x3 | false | 9,778 | [
"MIT"
] | 0 | d07dcbf026194b618a2bd9fc05b599563611f9a3 | https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
# AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
# according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | nmaac/acon | AconC | false | 16,178 | [
"MIT"
] | 163 | 99fd67928a6ffb0543b54614303caada96c756f5 | https://github.com/nmaac/acon/tree/99fd67928a6ffb0543b54614303caada96c756f5 |
OfstMapL1Loss | import torch
import torch.nn as nn
class OfstMapL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, rgb_labels, pred, gt, normalize=True, reduce=True):
wgt = (rgb_labels > 1e-08).float()
bs, n_kpts, c, h, w = pred.size()
... | 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... | StannisZhou/FFB6D | OfstMapL1Loss | false | 11,898 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
ConvSwishInplace | 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 ConvSwishInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishInplace, self).__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSwishInplace | false | 5,662 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CarnoZhao/mmsegmentation | DiceLoss | false | 7,860 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | xu94-nlp/Code-for-MAMO | Attention | false | 11,029 | [
"Apache-2.0"
] | 0 | d9c6655e0660976c90c07fa096a1f5dc8328a60b | https://github.com/xu94-nlp/Code-for-MAMO/tree/d9c6655e0660976c90c07fa096a1f5dc8328a60b |
AveragePooling | # 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... | GingerNg/SDNet | AveragePooling | false | 13,728 | [
"MIT"
] | 112 | 48ad8cc57c9a02aaad10e34d0c91a174ac68f056 | https://github.com/GingerNg/SDNet/tree/48ad8cc57c9a02aaad10e34d0c91a174ac68f056 |
ConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | kj21choi/LATAD | ConvLayer | false | 7,037 | [
"MIT"
] | 1 | 80d91e0f251ad0225342ee30e2461a39fa9cca97 | https://github.com/kj21choi/LATAD/tree/80d91e0f251ad0225342ee30e2461a39fa9cca97 |
Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | msank00/miniTransformer | Norm | false | 12,802 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LSaldyt/laser-dog | Net | false | 9,516 | [
"MIT"
] | 0 | 168c8bfea95dcd27a499f00f191232d67ae63c1c | https://github.com/LSaldyt/laser-dog/tree/168c8bfea95dcd27a499f00f191232d67ae63c1c |
Minibatch_stddev_layer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Iceland-Leo/StyleGAN2_PyTorch | Minibatch_stddev_layer | false | 5,322 | [
"MIT"
] | 1 | 3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 | https://github.com/Iceland-Leo/StyleGAN2_PyTorch/tree/3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 |
NTM | # 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.... | WuDiDaBinGe/TAKG | NTM | false | 1,899 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
CompositeActivation | import torch
class CompositeActivation(torch.nn.Module):
def forward(self, x):
x = torch.atan(x)
return torch.cat([x / 0.67, x * x / 0.6], 1)
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.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ndey96/lucent | CompositeActivation | false | 10,602 | [
"Apache-2.0"
] | 0 | d868d8ca52520bd245c1e5fcf3b026782f77e561 | https://github.com/ndey96/lucent/tree/d868d8ca52520bd245c1e5fcf3b026782f77e561 |
MaxPoolPad | # 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.utils.data
import torch.nn as nn
from torch import optim as optim
import tor... | Exir-lxr/crldr-prune-pytorch | MaxPoolPad | false | 2,286 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
SmoothL1Loss | # 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 functools
impor... | BUPT-PRIV/BalancedGroupSoftmax | SmoothL1Loss | false | 13,374 | [
"Apache-2.0"
] | 333 | 90e04fd8ccecd2bc61bbe6053a741ae708da2794 | https://github.com/BUPT-PRIV/BalancedGroupSoftmax/tree/90e04fd8ccecd2bc61bbe6053a741ae708da2794 |
CustomBatchNormAutograd | import torch
import torch.nn as nn
class CustomBatchNormAutograd(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does not n... | 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_... | askliar/deep_learning | CustomBatchNormAutograd | false | 1,488 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
UNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Aoi-hosizora/UNet-pytorch | UNet | false | 9,244 | [
"MIT"
] | 0 | 96951d5d1fdc6c6266a11e1bd97fbf72010bc87d | https://github.com/Aoi-hosizora/UNet-pytorch/tree/96951d5d1fdc6c6266a11e1bd97fbf72010bc87d |
SelfAttention | import torch
import torch.nn.functional as F
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, embedding_dimension, num_heads):
super().__init__()
assert embedding_dimension % num_heads == 0, f'embedding dimension must be divisible by number of heads, got embedding_dimension... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dimitrios-ebi/gene_symbol_classifier | SelfAttention | false | 12,289 | [
"Apache-2.0"
] | 0 | fe415f719fda4619041b9fe0639996c92e0f12a8 | https://github.com/dimitrios-ebi/gene_symbol_classifier/tree/fe415f719fda4619041b9fe0639996c92e0f12a8 |
SAModule_Head | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, use_bn=False, **kwargs):
super(BasicConv, self).__init__()
self.use_bn = use_bn
self.conv = nn.Conv2d(in_channels, out_channels, bias=not 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.... | vghost2008/C-3-Framework | SAModule_Head | false | 11,100 | [
"MIT"
] | 0 | dc6f1f67e403aff4dbb60f8ed06461c843407501 | https://github.com/vghost2008/C-3-Framework/tree/dc6f1f67e403aff4dbb60f8ed06461c843407501 |
InverseSqrt | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | awlange/pysurvival | InverseSqrt | false | 14,917 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
Embedder | # 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.nn import Module
i... | Zonglin-Li6565/FaceKoob | Embedder | false | 6,066 | [
"MIT"
] | 1 | d72da10330ec313308a16116b7d2abd8ecfcdbcf | https://github.com/Zonglin-Li6565/FaceKoob/tree/d72da10330ec313308a16116b7d2abd8ecfcdbcf |
ClassifierEnd | # 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.... | EadCat/Road-Extraction | ClassifierEnd | false | 17,246 | [
"MIT"
] | 4 | 9d4831b6c3a5ef07676cbe1c79b03045fda427ea | https://github.com/EadCat/Road-Extraction/tree/9d4831b6c3a5ef07676cbe1c79b03045fda427ea |
OnnxHardSigmoid | # 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... | ENOT-AutoDL/onnx2torch | OnnxHardSigmoid | false | 13,617 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
Affine | # 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 math
from torch import nn
import torch.autograd
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Goschjann/ssltsc | Affine | false | 17,309 | [
"MIT"
] | 5 | 08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e | https://github.com/Goschjann/ssltsc/tree/08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e |
DeConv2dBlock | # 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... | scaomath/galerkin-transformer | DeConv2dBlock | false | 16,370 | [
"MIT"
] | 106 | a9c2dc4427bfaba051d7e0154f110e460050c1df | https://github.com/scaomath/galerkin-transformer/tree/a9c2dc4427bfaba051d7e0154f110e460050c1df |
SPPblock | # 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_... | zxg3017/CUSE-Net | SPPblock | false | 13,207 | [
"MIT"
] | 0 | ea1d07027f89130a8a40465de94528f23eb9f5d1 | https://github.com/zxg3017/CUSE-Net/tree/ea1d07027f89130a8a40465de94528f23eb9f5d1 |
PerfectProd | import torch
import torch.utils.data
from torch import nn
class PerfectProd(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
def reset_parameters(self):
pass
def forward(self, x):
return torch.prod(2 * x[:, :-1], dim=-1, keepdim=True)
def get_input... | 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | hoedt/stable-nalu | PerfectProd | false | 3,597 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
TracedModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.quantization
import torch.onnx
import torch.nn.parallel
import tor... | MartinRenaudin/tutorials | TracedModule | false | 2,755 | [
"BSD-3-Clause"
] | 0 | 035d6827d77c52fed2a927f105e39fd73516f093 | https://github.com/MartinRenaudin/tutorials/tree/035d6827d77c52fed2a927f105e39fd73516f093 |
ConvBlock | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ZombaSY/DeepLPF | ConvBlock | false | 1,325 | [
"BSD-3-Clause"
] | 0 | adce64ae01bc9e32f465a354cb1f6534f0d13597 | https://github.com/ZombaSY/DeepLPF/tree/adce64ae01bc9e32f465a354cb1f6534f0d13597 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(x, (0, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HongBeenKim/pams-skku | MaxPoolStride1 | false | 17,376 | [
"MIT"
] | 8 | 0a12b132e4bf42570b000f60b9a1fc2c65382174 | https://github.com/HongBeenKim/pams-skku/tree/0a12b132e4bf42570b000f60b9a1fc2c65382174 |
MatchModule | import torch
import torch.nn.functional as F
from torch import nn
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> impor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | amberhuang01/LearningFromFactCheckers | MatchModule | false | 18,319 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
ScaleNorm | import math
import torch
import torch.nn as nn
import torch.nn.parallel
class ScaleNorm(nn.Module):
"""Apply Scale Normalization to input.
The ScaleNorm layer first computes the square root of the scale, then computes the matrix/vector norm of the input tensor.
The norm value is calculated as `sqrt(scale) / ... | 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 math
import torch.nn ... | JoseAntonioSiguenza/deepchem | ScaleNorm | false | 9,206 | [
"MIT"
] | 0 | 05fe1b186ec154e18de9aa1b110e9258dc484e21 | https://github.com/JoseAntonioSiguenza/deepchem/tree/05fe1b186ec154e18de9aa1b110e9258dc484e21 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | IanVzs/labelImg | AconC | false | 11,505 | [
"MIT"
] | 0 | 3d3dfbf9cf385f38c60376826fdce1f178f563a6 | https://github.com/IanVzs/labelImg/tree/3d3dfbf9cf385f38c60376826fdce1f178f563a6 |
Conv2dLayer | # 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 ... | autocomic/https-github.com-autocomic-DeepFillv2_Pytorch | Conv2dLayer | false | 3,149 | [
"MIT"
] | 0 | 7f6712a9b42dfd827879271f13856f1da5d6a032 | https://github.com/autocomic/https-github.com-autocomic-DeepFillv2_Pytorch/tree/7f6712a9b42dfd827879271f13856f1da5d6a032 |
SinglePITF_Loss | import torch
import torch as t
import torch.nn as nn
class SinglePITF_Loss(nn.Module):
"""
定义PITF的loss function
"""
def __init__(self):
super(SinglePITF_Loss, self).__init__()
None
def forward(self, r):
return t.sum(-t.log(t.sigmoid(r)))
def get_inputs():
return [to... | 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
... | SamHaoYuan/pitf | SinglePITF_Loss | false | 1,007 | [
"MIT"
] | 0 | 5fdebc3b44c6462126876101b052a3980804da79 | https://github.com/SamHaoYuan/pitf/tree/5fdebc3b44c6462126876101b052a3980804da79 |
ScaleNorm | import math
import torch
import torch.nn as nn
import torch.nn.parallel
class ScaleNorm(nn.Module):
"""Apply Scale Normalization to input.
The ScaleNorm layer first computes the square root of the scale, then computes the matrix/vector norm of the input tensor.
The norm value is calculated as `sqrt(scale) / ... | 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 math
import torch.nn ... | Chahalprincy/deepchem | ScaleNorm | false | 228 | [
"MIT"
] | 0 | 9d1a6a879cc74b065694b3ddb763d52151d57b7a | https://github.com/Chahalprincy/deepchem/tree/9d1a6a879cc74b065694b3ddb763d52151d57b7a |
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 import triton_helpers
from torch._inductor.runtime.... | openmynet/tract | MLP | false | 12,860 | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 0 | a9aba6edcfeacd34f781f08717ae374bfbaba80e | https://github.com/openmynet/tract/tree/a9aba6edcfeacd34f781f08717ae374bfbaba80e |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JunhoKim94/speech_hackathon_2019 | Attention | false | 687 | [
"Apache-2.0"
] | 0 | 1cb8de873d48e94f58bd1103c32b977a27d34951 | https://github.com/JunhoKim94/speech_hackathon_2019/tree/1cb8de873d48e94f58bd1103c32b977a27d34951 |
EnsembleModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | si0wang/transfer_dmc | EnsembleModel | false | 12,988 | [
"MIT"
] | 0 | 6bda773244e0b709b3c13add2597f5f1cd01bfd7 | https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JinYAnGHe/openvino_training_extensions | PositionwiseFeedForward | false | 2,724 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
Value | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | zwc662/disentangling-vae | Value | false | 11,085 | [
"MIT"
] | 0 | 7eeace2a30f8034e222be6a906f53748b3b2bb6e | https://github.com/zwc662/disentangling-vae/tree/7eeace2a30f8034e222be6a906f53748b3b2bb6e |
AdaptiveCos | import torch
from torch.nn.parameter import Parameter
class AdaptiveCos(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
... | 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.parameter import Parameter
assert_size_stride = torch._C._d... | ndem0/PINA | AdaptiveCos | false | 10,717 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
IDiv | import torch
class IDiv(torch.nn.Module):
def __init__(self):
super(IDiv, self).__init__()
def forward(self, x, y):
x /= y
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_div_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | NVIDIA-AI-IOT-private/torch2trt | IDiv | false | 10,512 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
EpeLoss | import torch
import torch.nn as nn
class EpeLoss(nn.Module):
def __init__(self, eps=0):
super(EpeLoss, self).__init__()
self.eps = eps
def forward(self, pred, label):
loss = ((pred - label).pow(2).sum(1) + self.eps).sqrt()
return loss.view(loss.shape[0], -1).mean(1)
def get... | 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_... | brightvioletlight/MaskFlownet-Pytorch | EpeLoss | false | 14,977 | [
"MIT"
] | 75 | 4158bac3b2fe50bfdf4216b4890ce24a8011227a | https://github.com/brightvioletlight/MaskFlownet-Pytorch/tree/4158bac3b2fe50bfdf4216b4890ce24a8011227a |
SimpleAndModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAndModule(torch.nn.Module):
def __init__(self):
super(SimpleAndModule, self).__init__()
def forward(self, a, b):
c = torch.logical_and(a, b)
return torch.logical_and(c, c)
def get_inputs():
return [torc... | 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 | SimpleAndModule | false | 7,389 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
SIMSE | import torch
import torch.nn as nn
import torch.utils.checkpoint
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo... | byamao1/MMSA | SIMSE | false | 14,986 | [
"MIT"
] | 198 | 1a894d042144c9ac75b3465d38871ce8c2987251 | https://github.com/byamao1/MMSA/tree/1a894d042144c9ac75b3465d38871ce8c2987251 |
FCLayer | import torch
from torch import nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=
True, is_dropout=True, active_type='mish'):
"""
FC-Layer, mostly last output of model
args:
input_dim: input dimension, 输入维度, eg. 768
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | dumpmemory/Pytorch-NLU | FCLayer | false | 15,260 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
GaussianGenerator | import torch
import numpy as np
import torch.nn as nn
class GaussianGenerator(nn.Module):
def __init__(self, dims):
super(GaussianGenerator, self).__init__()
self.z_dim = dims[0]
self.linear_var = nn.Parameter(1.0 * torch.ones([self.z_dim]))
self.bias = nn.Parameter(torch.zeros([s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | MichaelArbel/GeneralizedEBM | GaussianGenerator | false | 8,560 | [
"BSD-3-Clause"
] | 40 | b2fb244bacef23a7347aecc0e8ff4863153f94f0 | https://github.com/MichaelArbel/GeneralizedEBM/tree/b2fb244bacef23a7347aecc0e8ff4863153f94f0 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_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.... | HS-YN/PanoAVQA | BertSelfAttention | false | 18,376 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
FusedDownsample | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bias =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
assert_size_stride = torch._C._dynam... | KwonGihyun/DiagonalGAN | FusedDownsample | false | 8,443 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
Conv1dWeightNorm | import torch
import torch.nn as nn
class Conv1dWeightNorm(nn.Module):
"""
Conv1d with weight normalization
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv1dWeightNorm, self).__init__()
self.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.triton_helpers import libdevice
import torch.nn as ... | juheeuu/flowseq | Conv1dWeightNorm | false | 12,657 | [
"Apache-2.0"
] | 0 | e6e50406656335ff7a2f9ed4bd81d7cc7d1195fb | https://github.com/juheeuu/flowseq/tree/e6e50406656335ff7a2f9ed4bd81d7cc7d1195fb |
make_dilation_dense | # 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 ... | cestcedric/TSSR-GAN | make_dilation_dense | false | 1,661 | [
"BSD-2-Clause",
"MIT"
] | 0 | d6e1b50409e0f0591660552993e6d5b70d41e766 | https://github.com/cestcedric/TSSR-GAN/tree/d6e1b50409e0f0591660552993e6d5b70d41e766 |
SphericalBesselBasis | # 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 math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | krylea/ocp | SphericalBesselBasis | false | 10,497 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 |
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