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 |
|---|---|---|---|---|---|---|---|---|---|---|
InverseSigmoidTransformer | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
from torch.distributions.utils import probs_to_logits
class Bijection(nn.Module):
"""
An invertible transformation.
"""
def __init__(self):
super().__init__()
def forward(self, inputs, context):
... | 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... | probabll/dgm.pt | InverseSigmoidTransformer | false | 7,491 | [
"MIT"
] | 1 | 95b5b1eb798b87c3d621e7416cc1c423c076c865 | https://github.com/probabll/dgm.pt/tree/95b5b1eb798b87c3d621e7416cc1c423c076c865 |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | HerbertMcSnout/transformers_with_trees | ScaleNorm | false | 8,232 | [
"MIT"
] | 18 | 1afa6d4ad45207c9b2762600a9c227d721fbc825 | https://github.com/HerbertMcSnout/transformers_with_trees/tree/1afa6d4ad45207c9b2762600a9c227d721fbc825 |
SimpleAndModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleAndModule | false | 12,546 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
FFN | import torch
import torch.nn as nn
import torch as t
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of 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
from torch._inductor.runtime.... | Munna-Manoj/Team7_TTS | FFN | false | 11,730 | [
"MIT"
] | 0 | 5e2d473a2afe429023876bcc51c2ac966a4938b8 | https://github.com/Munna-Manoj/Team7_TTS/tree/5e2d473a2afe429023876bcc51c2ac966a4938b8 |
LayerHardtanh | import random
import torch
import torch.nn as nn
class LayerHardtanh(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerHardtanh, self).__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
self.htanh = 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 import triton_helpers
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | dawnclaude/onnx2keras | LayerHardtanh | false | 15,151 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
Recall | import torch
import torch.nn as nn
class Recall(nn.Module):
"""
This class implements the recall score. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
"""
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | Recall | false | 13,495 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
MMFB | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, groups=3):
super(ConvBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.groups = gro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | wwjfsfs/wwjyyds | MMFB | false | 13,231 | [
"MIT"
] | 0 | 80cd6267fde7cd98838078a0d5178a557ceb7414 | https://github.com/wwjfsfs/wwjyyds/tree/80cd6267fde7cd98838078a0d5178a557ceb7414 |
BertPreTrainingHeads | import torch
import torch.nn as nn
class BertPredictionHeadTransform(nn.Module):
def __init__(self, hidden_size, hidden_act=nn.GELU()):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.transform_act_fn = hidden_act
self.LayerNorm = nn.LayerNorm(hidden_size)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st | BertPreTrainingHeads | false | 17,789 | [
"Apache-2.0"
] | 4 | 6382433cda69c655f03c3cc284dc076407f18dc9 | https://github.com/PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st/tree/6382433cda69c655f03c3cc284dc076407f18dc9 |
Qnet | # 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 random
import torch.nn... | linklab/link_rl_book_codes | Qnet | false | 10,417 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 |
SmoothCrossEntropyLoss | # 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.nn.modules.... | bluetyson/archai | SmoothCrossEntropyLoss | false | 3,235 | [
"MIT"
] | 0 | b370a7397cb8703a052d82297ae748a35c6a49c7 | https://github.com/bluetyson/archai/tree/b370a7397cb8703a052d82297ae748a35c6a49c7 |
GCNdecoder | from torch.nn import Module
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch.nn import functional as F
class GCN(Module):
"""
Graph Convolutional Network
"""
def __init__(self, in_features, out_features, bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Roxbili/topoGAN | GCNdecoder | false | 5,772 | [
"MIT"
] | 1 | 25cc397bf8925e485d3a39837b8bce552118f5dc | https://github.com/Roxbili/topoGAN/tree/25cc397bf8925e485d3a39837b8bce552118f5dc |
ContrastiveLoss | import torch
import torch.optim
from typing import Any
from typing import NoReturn
import torch
import torch.nn as nn
class ContrastiveLoss(nn.Module):
""" 对比损失函数"""
def __init__(self) ->NoReturn:
super(ContrastiveLoss, self).__init__()
def forward(self, ew: 'Any', label: 'Any', m: '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
import torch.optim
from typing import NoReturn
import torch
import torch.nn as nn
assert_... | DengBoCong/text-sim | ContrastiveLoss | false | 7,945 | [
"MIT"
] | 21 | 2c6c323649aa259a7b3d5c6d3714bd1860114826 | https://github.com/DengBoCong/text-sim/tree/2c6c323649aa259a7b3d5c6d3714bd1860114826 |
SmallConvNet | import torch
from typing import Tuple
import torch.nn as nn
from numpy import prod
class SmallConvNet(nn.Module):
"""
A network with three conv layers. This is used for testing convolution
layers for activation count.
"""
def __init__(self, input_dim: 'int') ->None:
super(SmallConvNet, 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 typing import Tuple
import torch.nn as nn
from numpy import prod
assert_siz... | synthara/M-SFV-SyntharaFVcore | SmallConvNet | false | 10,900 | [
"Apache-2.0"
] | 0 | b4d2167a110aaecf3df442f58793ca2cb7b028ba | https://github.com/synthara/M-SFV-SyntharaFVcore/tree/b4d2167a110aaecf3df442f58793ca2cb7b028ba |
BiasAdd | from _paritybench_helpers import _mock_config
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class BiasAdd(nn.Module):
def __init__(self, channels, opts, act='linear', alpha=None, gain=None,
lrmul=1):
"""
BiasAdd
"""
super(BiasAdd... | 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... | tomguluson92/StyleGAN2_PyTorch | BiasAdd | false | 16,585 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
FusedDownsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | nazarblch/style-based-gan-pytorch | FusedDownsample | false | 4,055 | [
"MIT"
] | 0 | 5ed7fa114904501d77b414921cd9f439773ba24c | https://github.com/nazarblch/style-based-gan-pytorch/tree/5ed7fa114904501d77b414921cd9f439773ba24c |
PreNet | import torch
from torch import nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = 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 import nn
assert_s... | lsh950919/sv2tts | PreNet | false | 12,733 | [
"MIT"
] | 0 | a6ff637ac478b8b3ce4dcc5a776442cab9cbdd67 | https://github.com/lsh950919/sv2tts/tree/a6ff637ac478b8b3ce4dcc5a776442cab9cbdd67 |
Conv2d | import torch
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
def keep_variance_fn(x):
return x + 0.001
class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | THAKAORI/SalsaNext | Conv2d | false | 11,902 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
RevPaddingLayer | import torch
import torch.nn as nn
class RevPaddingLayer(nn.Module):
def __init__(self, stride):
super().__init__()
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
def forward(self, x):
x = self.pool(x)
zeros = torch.zeros_like(x)
zeros_left, zeros_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | RKorzeniowski/BigBiGAN-PyTorch | RevPaddingLayer | false | 17,832 | [
"MIT"
] | 5 | caaaf69b094ae45e9fa3608577fde32dafa1f16e | https://github.com/RKorzeniowski/BigBiGAN-PyTorch/tree/caaaf69b094ae45e9fa3608577fde32dafa1f16e |
GaussMembFunc | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class GaussMembFunc(torch.nn.Module):
"""
Gaussian membership functions, defined by two... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | samxu0823/anfis-pytorch | GaussMembFunc | false | 4,261 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c |
TemporalEmbedding | # 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Fanxingye/Informer2020 | TemporalEmbedding | false | 425 | [
"Apache-2.0"
] | 0 | 94fd05f82ff0882681a9716ae3e980a574fdcbed | https://github.com/Fanxingye/Informer2020/tree/94fd05f82ff0882681a9716ae3e980a574fdcbed |
SmoothL1Loss | # AOT ID: ['1_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
... | LiWentomng/boxlevelset | SmoothL1Loss | false | 8,470 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
EntropyLoss | import math
import torch
import torch.nn as nn
class EntropyLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, eps=1e-08):
logN = math.log(float(x.shape[0]))
x = x * (x + eps).log() / logN
neg_entropy = x.sum(1)
return -neg_entropy.mean()
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | pudumagico/deepproblog | EntropyLoss | false | 16,283 | [
"Apache-2.0"
] | 54 | 6d38e783990551f4030780a1d69c7138fada2020 | https://github.com/pudumagico/deepproblog/tree/6d38e783990551f4030780a1d69c7138fada2020 |
NormConv2d | # 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... | CompVis/interactive-image2video-synthesis | NormConv2d | false | 7,934 | [
"MIT"
] | 20 | 05ea449d3a2704b6d79a5f08683035220d615576 | https://github.com/CompVis/interactive-image2video-synthesis/tree/05ea449d3a2704b6d79a5f08683035220d615576 |
MultiHeadAttention | import torch
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout, inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | superMC5657/BiLSTMTransformer | MultiHeadAttention | false | 13,008 | [
"MIT"
] | 0 | 43aa7bb4d8831a898c79ea89fcb1d3f5e09d564a | https://github.com/superMC5657/BiLSTMTransformer/tree/43aa7bb4d8831a898c79ea89fcb1d3f5e09d564a |
PrecomputedNorm | import torch
import torch.nn as nn
class PrecomputedNorm(nn.Module):
"""Normalization using Pre-computed Mean/Std.
Args:
stats: Precomputed (mean, std).
axis: Axis setting used to calculate mean/variance.
"""
def __init__(self, stats, axis=[1, 2]):
super().__init__()
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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | czlwang/s3prl | PrecomputedNorm | false | 12,268 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
ResBlock | import torch
from torch.nn import functional as F
from torch import nn
from torch.nn.utils import spectral_norm
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super().__init__()
self.num_features = num_features
self.eps = eps
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.triton_helpers import libdevice
from torch.nn impor... | CompVis/interactive-image2video-synthesis | ResBlock | false | 7,944 | [
"MIT"
] | 20 | 05ea449d3a2704b6d79a5f08683035220d615576 | https://github.com/CompVis/interactive-image2video-synthesis/tree/05ea449d3a2704b6d79a5f08683035220d615576 |
DiaynDiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiaynDiscrimNet(nn.Module):
def __init__(self, f_spa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AswinRetnakumar/Machina | DiaynDiscrimNet | false | 13,316 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aeloyq/EasyTransfer | BertSelfAttention | false | 14,755 | [
"Apache-2.0"
] | 806 | f02b1f40109c4031632f3c51bce1cf3d1e906e34 | https://github.com/aeloyq/EasyTransfer/tree/f02b1f40109c4031632f3c51bce1cf3d1e906e34 |
GraphAttention | # 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.... | Supermaxman/covid19-data | GraphAttention | false | 9,602 | [
"Apache-2.0"
] | 0 | 13e8e0c30a063c60e2160896458cd290a85ea0e2 | https://github.com/Supermaxman/covid19-data/tree/13e8e0c30a063c60e2160896458cd290a85ea0e2 |
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... | FynnBe/tiktorch | TinyConvNet2d | false | 11,432 | [
"MIT"
] | 0 | 60c6fa9700e7ff73e44338e8755c56c6e8846f2f | https://github.com/FynnBe/tiktorch/tree/60c6fa9700e7ff73e44338e8755c56c6e8846f2f |
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 import... | Josie-Li/ZazuML-easy_AutoML | ShakeResNeXt | false | 2,436 | [
"MIT"
] | 0 | e4daabaab9df518c35abdba35a67607d002bee33 | https://github.com/Josie-Li/ZazuML-easy_AutoML/tree/e4daabaab9df518c35abdba35a67607d002bee33 |
CoAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mayankiitg/cs224n | CoAttention | false | 4,006 | [
"MIT"
] | 0 | c67b7904101c8f19a5a231e4fe521e764470d41b | https://github.com/mayankiitg/cs224n/tree/c67b7904101c8f19a5a231e4fe521e764470d41b |
AdMSoftmaxLoss | # 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.... | B06901052/s3prl | AdMSoftmaxLoss | false | 102 | [
"MIT"
] | 0 | 5f63d2df043d2d7c81580cd042fa2cea34746f48 | https://github.com/B06901052/s3prl/tree/5f63d2df043d2d7c81580cd042fa2cea34746f48 |
PositionGenerator | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | eweiner/MAT_Extension | PositionGenerator | false | 12,367 | [
"MIT"
] | 0 | 505884a67f97bf54e1198077d15a48531fcac7a5 | https://github.com/eweiner/MAT_Extension/tree/505884a67f97bf54e1198077d15a48531fcac7a5 |
GroupedMultiHeadAttention | # 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.... | gheyret/EfficientConformer | GroupedMultiHeadAttention | false | 15,427 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
ResnetBlockFC | import torch
import torch.nn as nn
import torch.autograd.profiler as profiler
class ResnetBlockFC(nn.Module):
"""
Fully connected ResNet Block class.
Taken from DVR code.
:param size_in (int): input dimension
:param size_out (int): output dimension
:param size_h (int): hidden 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
import torch.nn as nn
assert_... | HexagonPrime/pixel-nerf | ResnetBlockFC | false | 2,418 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
SelfAttentionPooling | # 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.... | chiluen/s3prl | SelfAttentionPooling | false | 1,698 | [
"Apache-2.0"
] | 0 | c81838f6414d3c4767de355144449e40f86c7066 | https://github.com/chiluen/s3prl/tree/c81838f6414d3c4767de355144449e40f86c7066 |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, activation='relu'):
super().__init__()
self.in_channels, self.out_channels, self.activation = (in_channels,
out_channels, activation)
self.blocks = nn.Identity()
... | 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... | PaParaZz1/DI-engine | ResidualBlock | false | 11,852 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
GRUStep | # 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 ... | siyangZhao/BAMnet | GRUStep | false | 16,454 | [
"Apache-2.0"
] | 170 | 4c6222610c120a4a114daf40938219ea0ca57dc6 | https://github.com/siyangZhao/BAMnet/tree/4c6222610c120a4a114daf40938219ea0ca57dc6 |
MatrixReduceMin | import torch
import torch.nn as nn
import torch.autograd
class MatrixReduceMin(nn.Module):
def __init__(self):
super(MatrixReduceMin, self).__init__()
def forward(self, x):
z = torch.min(x)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixReduceMin | false | 14,344 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
SurfaceLoss | import torch
import torch.nn as nn
class SurfaceLoss(nn.Module):
def __init__(self, epsilon=1e-05, softmax=True):
super(SurfaceLoss, self).__init__()
self.weight_map = []
def forward(self, x, distmap):
x = torch.softmax(x, dim=1)
self.weight_map = distmap
score = x.fl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | kbarkevich/RITnet | SurfaceLoss | false | 7,015 | [
"MIT"
] | 1 | 5df66c656734aecd2987cf27d9359416b136af2e | https://github.com/kbarkevich/RITnet/tree/5df66c656734aecd2987cf27d9359416b136af2e |
SimpleCeilModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCeilModule(torch.nn.Module):
def forward(self, a, b):
c = a + b
return torch.ceil(c)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | briancoutinho/glow | SimpleCeilModule | false | 12,568 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
DepthConv2d | # 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
... | rbodo/pytorch-OpCounter | DepthConv2d | false | 7,549 | [
"MIT"
] | 1 | 1857cbb5f9e53343fb349af84efdfde2554a2691 | https://github.com/rbodo/pytorch-OpCounter/tree/1857cbb5f9e53343fb349af84efdfde2554a2691 |
BoundPow | 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 numbers import Number
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional... | Mahoumaru/auto_LiRPA | BoundPow | false | 13,222 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
_ImpalaBlock | # 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... | nrfulton/vsrl-framework | _ImpalaBlock | false | 7,369 | [
"MIT"
] | 1 | c778824b3285e3e994a4c5846c7b1c2ac03c669b | https://github.com/nrfulton/vsrl-framework/tree/c778824b3285e3e994a4c5846c7b1c2ac03c669b |
ClsHead | # 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.... | BHD233/PaddleOCR2Pytorch | ClsHead | false | 13,358 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
CrossEntropyLossWithSoftLabel | import torch
from torch.nn import *
from torch.optim import *
from torch.optim.lr_scheduler import *
class CrossEntropyLossWithSoftLabel(torch.nn.Module):
def __init__(self, reduction='mean'):
super().__init__()
self.reduction = reduction
self.logsoftmax = torch.nn.LogSoftmax(dim=1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import *... | UNIST-LIM-Lab/NeuBoots | CrossEntropyLossWithSoftLabel | false | 2,913 | [
"MIT"
] | 0 | 196adf9e1ece2abc145f69966504bac2676e5b5e | https://github.com/UNIST-LIM-Lab/NeuBoots/tree/196adf9e1ece2abc145f69966504bac2676e5b5e |
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.triton_helpers import libdevice
import math
import ... | akakakakakaa/pytorchic-bert | PositionWiseFeedForward | false | 4,725 | [
"Apache-2.0"
] | 0 | 055d72adce9a41c322d23145840f31a94d9ffec4 | https://github.com/akakakakakaa/pytorchic-bert/tree/055d72adce9a41c322d23145840f31a94d9ffec4 |
BertOutput | # 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... | zdxdsw/WebQA_VLP | BertOutput | false | 11,053 | [
"Apache-2.0"
] | 0 | 443bcd7e9b36db47e2ab4502abaaa3724800f394 | https://github.com/zdxdsw/WebQA_VLP/tree/443bcd7e9b36db47e2ab4502abaaa3724800f394 |
LearnedPositionalEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring 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... | William-Zhanng/Protein_affinity | LearnedPositionalEmbedding | false | 5,977 | [
"MIT"
] | 1 | 8abd12073b182274bf464ff23fd3be406c4e39ac | https://github.com/William-Zhanng/Protein_affinity/tree/8abd12073b182274bf464ff23fd3be406c4e39ac |
UpConv2D | import torch
import torch.nn as nn
class UpConv2D(nn.Module):
def __init__(self, in_channels=3, out_channels=3, kernel_size=5, ratio=2):
super(UpConv2D, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels * ratio ** 2,
kernel_size, padding=kernel_size // 2)
self.u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | emirkonuk/defocus | UpConv2D | false | 3,468 | [
"Apache-2.0"
] | 0 | da2977d2698eb20e9ab2a3bcd1fa4d05e1dd9b50 | https://github.com/emirkonuk/defocus/tree/da2977d2698eb20e9ab2a3bcd1fa4d05e1dd9b50 |
DQN_hot3 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot3(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self, m, n, num_actions):
super(DQN_hot3, self).__init__()
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_hot3 | false | 17,176 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
ZeroModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.random
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | TaoHuang13/imitation | ZeroModule | false | 9,508 | [
"MIT"
] | 0 | f979be0fa05106754f6d1e5a98495d0fedbea598 | https://github.com/TaoHuang13/imitation/tree/f979be0fa05106754f6d1e5a98495d0fedbea598 |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
def _is_long(x):
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representati... | 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
... | Randl/Ranger_Mish_reimplementation | CrossEntropyLoss | false | 17,838 | [
"MIT"
] | 7 | 36f580ce8a02fae1929e101c9bd6987ccd2a5843 | https://github.com/Randl/Ranger_Mish_reimplementation/tree/36f580ce8a02fae1929e101c9bd6987ccd2a5843 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
return 1 - (2 * torch.sum(output * target) + self.smooth) / (torch.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | kant/open-solution-ship-detection | DiceLoss | false | 12,655 | [
"MIT"
] | 0 | 94fa14fc461d6088d884930cbd8e2a2b99a338b5 | https://github.com/kant/open-solution-ship-detection/tree/94fa14fc461d6088d884930cbd8e2a2b99a338b5 |
MinimaxDiscriminatorLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, red... | 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... | shi-weili/torchgan | MinimaxDiscriminatorLoss | false | 12,972 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
VNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class VNet(nn.Module):
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | KtechB/machina | VNet | false | 2,475 | [
"MIT"
] | 0 | 24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab | https://github.com/KtechB/machina/tree/24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab |
AE | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
class AE(nn.Module):
""" Autoencoder for dimensional reduction"""
def __init__(self, dim):
super(AE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, 512)
self.fc2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | peterfeifanchen/scGNN | AE | false | 16,230 | [
"MIT"
] | 60 | 4ef9013ad0f44f9f51708e9bb60e5138f5706593 | https://github.com/peterfeifanchen/scGNN/tree/4ef9013ad0f44f9f51708e9bb60e5138f5706593 |
GlobalAttentionGeneral | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.onnx
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class GlobalAttentionGeneral(nn.Module):
def __... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MaxyLee/Style-AttnGAN | GlobalAttentionGeneral | false | 8,528 | [
"MIT"
] | 36 | d33d0df061c94b75ad4af5c750b8d6f37ee1a35a | https://github.com/MaxyLee/Style-AttnGAN/tree/d33d0df061c94b75ad4af5c750b8d6f37ee1a35a |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | dumpmemory/Pytorch-NLU | FocalLoss | false | 15,242 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
my_Hingeloss | import torch
import torch.nn as nn
class my_Hingeloss(nn.Module):
def __init__(self):
super(my_Hingeloss, self).__init__()
def forward(self, output, target):
pos = torch.sum(output * target, 2)
neg = torch.max((1 - target) * output, 2)
loss = neg[0] - pos + 1
loss[los... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | carsault/chord_sequence_prediction | my_Hingeloss | false | 1,635 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 |
InstanceNorm2dPlus | # 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_... | DeepTitan/PNDM | InstanceNorm2dPlus | false | 13,584 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
norm = torch.norm(x, p=2, dim=self.dim, keepdim=True)
return x / norm
def get_inputs():
return [torch.rand([4, 4, 4, 4, 4])]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | phuochieu212/PointGLR | Normalize | false | 16,249 | [
"MIT"
] | 104 | 37017b1af31486aa9d516a3762725a650dca9ad1 | https://github.com/phuochieu212/PointGLR/tree/37017b1af31486aa9d516a3762725a650dca9ad1 |
TemperatureHolder | import torch
from torch import nn
class TemperatureHolder(nn.Module):
"""Module that holds a temperature as a learnable value.
Args:
initial_log_temperature (float): Initial value of log(temperature).
"""
def __init__(self, initial_log_temperature=0):
super().__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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | g-votte/pfrl | TemperatureHolder | false | 15,393 | [
"MIT"
] | 824 | 4c30c1d73f0941a2b649b62937eec346bb55a95e | https://github.com/g-votte/pfrl/tree/4c30c1d73f0941a2b649b62937eec346bb55a95e |
ActionApproximation | import torch
class ActionApproximation(torch.nn.Module):
def __init__(self, state_observations_count, action_count, hidden_count=512
):
super(ActionApproximation, self).__init__()
self.ReLU = torch.nn.ReLU()
self.dense0 = torch.nn.Linear(state_observations_count, hidden_count)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Unn20/achtung_die_kurve | ActionApproximation | false | 2,920 | [
"MIT"
] | 0 | e2dbb1752c070cfc398e415d5a427384c0230f3c | https://github.com/Unn20/achtung_die_kurve/tree/e2dbb1752c070cfc398e415d5a427384c0230f3c |
BboxHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._utils
from itertools import product as produ... | Capetian/FaceX-Zoo | BboxHead | false | 5,025 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
ConvolutionModule | # 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 T... | thangdepzai/icefall | ConvolutionModule | false | 13,037 | [
"Apache-2.0"
] | 0 | 8c7995d493c4309c3d09bdabfa1ab12b4eec2657 | https://github.com/thangdepzai/icefall/tree/8c7995d493c4309c3d09bdabfa1ab12b4eec2657 |
ToTensor | from torch.nn import Module
import torch
class ToTensor(Module):
def __init__(self):
super(ToTensor, self).__init__()
def forward(self, x):
x = x / 255
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | alinavalinav/finn | ToTensor | false | 6,198 | [
"BSD-3-Clause"
] | 1 | e443a5859066a410a63c08dcfec4a90527ca24be | https://github.com/alinavalinav/finn/tree/e443a5859066a410a63c08dcfec4a90527ca24be |
LinearCombine | # 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
import torch.nn.parallel
import torch.optim
import ... | HarshCasper/nni | LinearCombine | false | 5,284 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
CategoricalSampler | import torch
import torch.nn as nn
class Sampler(nn.Module):
""" args; logits: (batch, n_nodes)
return; next_node: (batch, 1)
TopKSampler <=> greedy; sample one with biggest probability
CategoricalSampler <=> sampling; randomly sample one from possible distribution based on probability
"""
def __init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | daunfamily/VRP_MHA | CategoricalSampler | false | 12,299 | [
"MIT"
] | 0 | 9c23d181d11dbbacac01299c6e8931b8e266b9b4 | https://github.com/daunfamily/VRP_MHA/tree/9c23d181d11dbbacac01299c6e8931b8e266b9b4 |
AbsModel | # 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 import Module
from torch import Tensor
from torch.nn import... | nuwangunasekara/avalanche | AbsModel | false | 4,094 | [
"MIT"
] | 0 | 1f4d5b3e559552394cce573a85b1c9af26a544fb | https://github.com/nuwangunasekara/avalanche/tree/1f4d5b3e559552394cce573a85b1c9af26a544fb |
MSE_disc | import torch
import torch.nn as nn
class MSE_disc(nn.Module):
def __init__(self, weight_list=None):
super(MSE_disc, self).__init__()
self.weight_list = weight_list
def forward(self, x, labels):
loss = (x - labels) ** 2
if self.weight_list is not None:
loss = loss ... | 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... | Sampson-Lee/SIB-Net | MSE_disc | false | 2,809 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
PseudoCoord | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch | PseudoCoord | false | 13,933 | [
"MIT"
] | 298 | 52e1ba5a7f3b88c617115ccc755e2e7868e8de2b | https://github.com/KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch/tree/52e1ba5a7f3b88c617115ccc755e2e7868e8de2b |
DotProductSimilarity | import math
import torch
import torch.nn as nn
class SimilarityFunction(nn.Module):
"""
A ``SimilarityFunction`` takes a pair of tensors with the same shape, and computes a similarity
function on the vectors in the last dimension. For example, the tensors might both have shape
`(batch_size, sentence_... | 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... | michiyasunaga/GreaseLM | DotProductSimilarity | false | 16,041 | [
"MIT"
] | 76 | 596aa5047841e3e97730f621a2e4576772733df2 | https://github.com/michiyasunaga/GreaseLM/tree/596aa5047841e3e97730f621a2e4576772733df2 |
MutualBiAffineAttention | # 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, math as tl_math
im... | Maxi-0902/DRAN | MutualBiAffineAttention | false | 833 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
ODEfunc | # 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.... | Lauu1023/torchdiffeq | ODEfunc | false | 9,356 | [
"MIT"
] | 0 | f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 | https://github.com/Lauu1023/torchdiffeq/tree/f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 |
DGN | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttModel(nn.Module):
def __init__(self, din, hidden_dim, dout):
super(AttModel, self).__init__()
self.fcv = nn.Linear(din, hidden_dim)
self.fck = nn.Linear(din, hidden_dim)
self.fcq = nn.Linear(din, hidden_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HuangHaoyu1997/pytorch_DGN | DGN | false | 13,801 | [
"MIT"
] | 48 | f1b1a157a9b1678f9238f64458f44412b796d00e | https://github.com/HuangHaoyu1997/pytorch_DGN/tree/f1b1a157a9b1678f9238f64458f44412b796d00e |
SoftBinaryCrossEntropyLoss | # 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... | crobbins327/semanticGAN_WSI | SoftBinaryCrossEntropyLoss | false | 1,748 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 |
SoftEntropy | # 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
f... | Dingyuan-Zheng/ctf-UDA | SoftEntropy | false | 377 | [
"MIT"
] | 0 | 3e3c67f68d7eb0b52a16a259e5a77e153062c4fd | https://github.com/Dingyuan-Zheng/ctf-UDA/tree/3e3c67f68d7eb0b52a16a259e5a77e153062c4fd |
FirstLSTMAmp | import torch
import torch.utils.data
import torch.nn as nn
class FirstLSTMAmp(nn.Module):
"""
First LSTM amplifier branch.
Parameters:
----------
in_features : int
Number of input channels.
out_features : int
Number of output channels.
"""
def __init__(self, in_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | HyperGAN/imgclsmob | FirstLSTMAmp | false | 17,679 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
ResidualBlock_noBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | AbnerVictor/HCFlow | ResidualBlock_noBN | false | 9,106 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b |
MSELoss2d | # 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
... | mimiliaogo/DACS | MSELoss2d | false | 16,092 | [
"MIT"
] | 59 | 9f13e32566c293de560df4848b23631d9e11cf32 | https://github.com/mimiliaogo/DACS/tree/9f13e32566c293de560df4848b23631d9e11cf32 |
DiceLoss | import torch
import torch.nn as nn
import torch.hub
def dice_loss(input, target):
smooth = 1.0
input = torch.sigmoid(input)
if input.dim() == 4:
B, C, _H, _W = input.size()
iflat = input.view(B * C, -1)
tflat = target.view(B * C, -1)
else:
assert input.dim() == 3
... | 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.hub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | thangnx183/kaggle-understanding-clouds | DiceLoss | false | 16,575 | [
"BSD-2-Clause"
] | 207 | 15ad2a9029958262437b899cb00525579da23911 | https://github.com/thangnx183/kaggle-understanding-clouds/tree/15ad2a9029958262437b899cb00525579da23911 |
MeanVoxelFeatureExtractor | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | GuilinZ/PCDet | MeanVoxelFeatureExtractor | false | 2,312 | [
"Apache-2.0"
] | 0 | f39769160854871bec9954630b9a4369b603391d | https://github.com/GuilinZ/PCDet/tree/f39769160854871bec9954630b9a4369b603391d |
LogLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import MSELoss... | aykuttasil/mindsdb | LogLoss | false | 6,297 | [
"MIT"
] | 1 | 2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad | https://github.com/aykuttasil/mindsdb/tree/2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad |
KLDivLoss | # 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.nn import M... | techthiyanes/annotated_deep_learning_paper_implementations | KLDivLoss | false | 16,555 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
RUM | import torch
import torch.nn.functional as F
import torch.nn as nn
def rotation_components(x, y, eps=1e-12):
size_batch = x.size()[0]
hidden_size = x.size()[1]
u = F.normalize(x, p=2, dim=1, eps=eps)
costh = torch.sum(u * F.normalize(y, p=2, dim=1, eps=eps), dim=1).view(
size_batch, 1)
sin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | omri123/rotational-unit-of-memory | RUM | false | 16,262 | [
"MIT"
] | 82 | e796c841e1e837df09497ba77c3bc285db47d02d | https://github.com/omri123/rotational-unit-of-memory/tree/e796c841e1e837df09497ba77c3bc285db47d02d |
BertOutAttention | # 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.... | YanyuanQiao/HOP-VLN | BertOutAttention | false | 18,144 | [
"MIT"
] | 8 | 4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | https://github.com/YanyuanQiao/HOP-VLN/tree/4b26b2569afb3e7eb7d8c2ed814cd424e41cbade |
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.... | BlackNoodle/TUCORE-GCN | MultiHeadAttention | false | 7,808 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
ConvAE | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ShulingTang/DSC-Net | ConvAE | false | 9,505 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self, inplace=True):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.mul_(x.sigmoid()) if self.inplace else x.mul(x.sigmoid())
def get_inputs():
return [torch.rand([4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_pt... | ShowLo/Networks | Swish | false | 1,056 | [
"MIT"
] | 0 | 48f8545783966c383b6c3b600fbe37a15ea8ae3c | https://github.com/ShowLo/Networks/tree/48f8545783966c383b6c3b600fbe37a15ea8ae3c |
Auto_Encoder_Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Auto_Encoder_Model(nn.Module):
def __init__(self):
super(Auto_Encoder_Model, self).__init__()
self.conv1 = nn.Conv2d(1, 64, padding=1, kernel_size=3)
self.max_pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(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
import ... | NNDEV1/QandMedicAid | Auto_Encoder_Model | false | 5,649 | [
"MIT"
] | 1 | f229f7dcf192fd79715eba07a2e5121a13c7a571 | https://github.com/NNDEV1/QandMedicAid/tree/f229f7dcf192fd79715eba07a2e5121a13c7a571 |
SimpleFmodModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | mciprian13/glow | SimpleFmodModule | false | 3,998 | [
"Apache-2.0"
] | 0 | 90f88205d9bf8baff8df5bbda51c9d138e3e668b | https://github.com/mciprian13/glow/tree/90f88205d9bf8baff8df5bbda51c9d138e3e668b |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | b19e93n/PLC-Pyramid | DecoderLayer | false | 3,175 | [
"MIT"
] | 0 | 6d5b57be6995a94ef7402144cee965862713b031 | https://github.com/b19e93n/PLC-Pyramid/tree/6d5b57be6995a94ef7402144cee965862713b031 |
Conv3d_wd | # 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.... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | Conv3d_wd | false | 15,768 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
TestPointLSTM | # 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.... | evanfebrianto/pointlstm_gesture_recognition_pytorch | TestPointLSTM | false | 15,329 | [
"Apache-2.0"
] | 69 | 797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | https://github.com/evanfebrianto/pointlstm_gesture_recognition_pytorch/tree/797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 |
CNN_DropOut | import torch
from torch import nn
import torch.utils
class CNN_DropOut(torch.nn.Module):
"""
Recommended model by "Adaptive Federated Optimization" (https://arxiv.org/pdf/2003.00295.pdf)
Used for EMNIST experiments.
When `only_digits=True`, the summary of returned model is
```
Model:
_____... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | MichaelLee-ceo/FedSAUC | CNN_DropOut | false | 5,597 | [
"Apache-2.0"
] | 1 | 8c00008772213562ff6a07bf9fa92c3831713118 | https://github.com/MichaelLee-ceo/FedSAUC/tree/8c00008772213562ff6a07bf9fa92c3831713118 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | Randl/TResNet | SEModule | false | 5,755 | [
"Apache-2.0"
] | 1 | 18514caf61d77c7e000a71dde9d1f86ba792b38d | https://github.com/Randl/TResNet/tree/18514caf61d77c7e000a71dde9d1f86ba792b38d |
UpsampleConv2d | from torch.nn import Module
import math
import torch
from torchvision.datasets import *
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.modules.utils import _pair
from torchvision.transforms import *
class UpsampleConv2d(Module):
"""
To avoid the checkerboard artifacts of standard... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 math
from torchvision.datasets import *
from ... | ruijieren98/DANet | UpsampleConv2d | false | 16,358 | [
"MIT"
] | 2,190 | e38d61e371179833c08888fd5a1ee444cf5bd875 | https://github.com/ruijieren98/DANet/tree/e38d61e371179833c08888fd5a1ee444cf5bd875 |
Encoder | # 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.... | rorymaizels/AC299r | Encoder | false | 7,581 | [
"MIT"
] | 1 | eb4b76ad52a10b9af0579ec3f725ec8fc90b00f1 | https://github.com/rorymaizels/AC299r/tree/eb4b76ad52a10b9af0579ec3f725ec8fc90b00f1 |
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