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 |
|---|---|---|---|---|---|---|---|---|---|---|
ScaledDotProductAttentionMemory | # 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.... | mandaltanmoy1938/VisualGPT | ScaledDotProductAttentionMemory | false | 16,005 | [
"MIT"
] | 86 | 9ba78948282fdca502d5030f4eccc3df562982c3 | https://github.com/mandaltanmoy1938/VisualGPT/tree/9ba78948282fdca502d5030f4eccc3df562982c3 |
DeterministicCriticNet | import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class BasicNet:
def __init__(self, optimizer_fn, gpu, LSTM=False):
self.gpu = gpu and torch.cuda.is_available()
self.LSTM = LSTM
if self.gpu:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | G-Flor/deeprl | DeterministicCriticNet | false | 5,176 | [
"Apache-2.0"
] | 1 | aeae2c5d585e5853dc638968b1f090eb60abd351 | https://github.com/G-Flor/deeprl/tree/aeae2c5d585e5853dc638968b1f090eb60abd351 |
CosMargin | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CosMargin(nn.Module):
def __init__(self, in_size, out_size, s=None, m=0.0):
super(CosMargin, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.W = nn.Parameter(torch.randn(out_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | belphegor2211/khoa_luan | CosMargin | false | 9,985 | [
"MIT"
] | 0 | c9c163ebf3aff3005639ce7e4020e510295d1c75 | https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75 |
ComplexConvTranspose2d | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ComplexConvTranspose2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, output_padding=0, dilation=1, groups=1, bias=Tru... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.onnx.operators
import... | IIP-Sogang/Audio-Visual-Speech-Recognition | ComplexConvTranspose2d | false | 17,426 | [
"MIT"
] | 9 | bd03be91135acbc6162b83092d462b7fe71dd007 | https://github.com/IIP-Sogang/Audio-Visual-Speech-Recognition/tree/bd03be91135acbc6162b83092d462b7fe71dd007 |
Residual | # 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.... | SkywalkerAtlas/HRGAN | Residual | false | 5,839 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
EncoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class FeedF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | xurantju/densecap | EncoderLayer | false | 11,052 | [
"BSD-3-Clause"
] | 0 | 2e58501e453bf98b9cc892e5b64997f5c1dfc808 | https://github.com/xurantju/densecap/tree/2e58501e453bf98b9cc892e5b64997f5c1dfc808 |
Scale_and_shift | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BCV-Uniandes/SAMA | Scale_and_shift | false | 128 | [
"BSD-3-Clause"
] | 0 | 4c732c71486af17efed17480e363298cb65c851f | https://github.com/BCV-Uniandes/SAMA/tree/4c732c71486af17efed17480e363298cb65c851f |
TAGConv | import torch
import torch.nn as nn
class TAGConv(nn.Module):
def __init__(self, in_features, out_features, K=3, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.w = nn.Linear(in_features * (self.K + 1), out_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | EdisonLeeeee/Graphgallery | TAGConv | false | 5,114 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
CoxPHLoss | # 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, split_scan_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 ... | gabrielasuchopar/pycox | CoxPHLoss | false | 3,513 | [
"BSD-2-Clause"
] | 0 | e4ea5f0ee26c6d3e3a468f164de2b7c426376e99 | https://github.com/gabrielasuchopar/pycox/tree/e4ea5f0ee26c6d3e3a468f164de2b7c426376e99 |
LC_SEModule | import torch
import torch.nn as nn
import torch.utils.data
class LC_SEModule(nn.Module):
def __init__(self, channel, reduction=4):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=channel, out_channels=channel //
reduction, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | COEN-390/YOLOv5-Lite | LC_SEModule | false | 11,273 | [
"MIT"
] | 0 | 06a53f5d001c5d37729f55f47cbd46cc8eb63f84 | https://github.com/COEN-390/YOLOv5-Lite/tree/06a53f5d001c5d37729f55f47cbd46cc8eb63f84 |
PredictionHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.gu... | ephrem-git/inference | PredictionHead | false | 12,350 | [
"Apache-2.0"
] | 0 | bfbda5fc419364c3f71b5b1640f6c00e7675b212 | https://github.com/ephrem-git/inference/tree/bfbda5fc419364c3f71b5b1640f6c00e7675b212 |
GlobalAttention | # 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.... | Katarina11/PreSumm | GlobalAttention | false | 11,612 | [
"MIT"
] | 0 | 616e72f038d512e9e9112af375d66a0b2e3db6cd | https://github.com/Katarina11/PreSumm/tree/616e72f038d512e9e9112af375d66a0b2e3db6cd |
Mish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | absallh/A_yolov3 | Mish | false | 18,215 | [
"Apache-2.0"
] | 6 | 550ec41de42b8efe638e887c51a568189947e049 | https://github.com/absallh/A_yolov3/tree/550ec41de42b8efe638e887c51a568189947e049 |
GoodDiscriminator | # 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.... | justaboutlola/improved-wgan-pytorch | GoodDiscriminator | false | 16,210 | [
"MIT"
] | 412 | 5bb0b729809152d9129ef72a9dd28b3ff83021a2 | https://github.com/justaboutlola/improved-wgan-pytorch/tree/5bb0b729809152d9129ef72a9dd28b3ff83021a2 |
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 ... | odb9402/MAT | PositionGenerator | false | 4,111 | [
"MIT"
] | 0 | 95d8083170da2c8ce1f5898b3a556bcf54eac8cc | https://github.com/odb9402/MAT/tree/95d8083170da2c8ce1f5898b3a556bcf54eac8cc |
Envelope | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | douglasrizzo/pytorch_geometric | Envelope | false | 12,298 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
StyledConv | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=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.triton_helpers import libdevice
import math
from to... | YotamNitzan/pixel2style2pixel | StyledConv | false | 2,995 | [
"MIT"
] | 0 | b943f9e6de046a54b901eea1d8714cb02a71605f | https://github.com/YotamNitzan/pixel2style2pixel/tree/b943f9e6de046a54b901eea1d8714cb02a71605f |
GTXCrossAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rsgit95/med_kg_txt_multimodal | GTXCrossAttentionLayer | false | 4,220 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
ConsensusAttention | import torch
import torch.nn.functional as F
from torch import nn
from torch import einsum
class ConsensusAttention(nn.Module):
def __init__(self, num_patches_side, attend_self=True,
local_consensus_radius=0):
super().__init__()
self.attend_self = attend_self
self.local_consensus_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Tahlor/glom-pytorch | ConsensusAttention | false | 1,130 | [
"MIT"
] | 0 | 45b2fc52af5288cd53611e497a70d53ffa303410 | https://github.com/Tahlor/glom-pytorch/tree/45b2fc52af5288cd53611e497a70d53ffa303410 |
TorchDiceLoss | # 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... | Spiruel/solaris | TorchDiceLoss | false | 11,944 | [
"Apache-2.0"
] | 0 | eb2ce05265a462d69b01ee2b621a85a3e9082402 | https://github.com/Spiruel/solaris/tree/eb2ce05265a462d69b01ee2b621a85a3e9082402 |
AtLocPlusCriterion | import torch
import torch.nn as nn
import torch.nn.init
def calc_vos_simple(poses):
vos = []
for p in poses:
pvos = [(p[i + 1].unsqueeze(0) - p[i].unsqueeze(0)) for i in range(
len(p) - 1)]
vos.append(torch.cat(pvos, dim=0))
vos = torch.stack(vos, dim=0)
return vos
class ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C.... | xunshengliuyin/ATwvo | AtLocPlusCriterion | false | 4,611 | [
"MIT"
] | 0 | 7d8b7aeb7893cb59d48864a9a35f7de9dce084b4 | https://github.com/xunshengliuyin/ATwvo/tree/7d8b7aeb7893cb59d48864a9a35f7de9dce084b4 |
Correct | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda ... | aoranwu/grace | Correct | false | 14,881 | [
"BSD-2-Clause"
] | 88 | 1e28915f6f6e8189ef33c0c7d8d3ce314e0a493e | https://github.com/aoranwu/grace/tree/1e28915f6f6e8189ef33c0c7d8d3ce314e0a493e |
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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Hcnaeg/DI-engine | Encoder | false | 2,385 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
FinalPool | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | praesc/end-to-end-SLU | FinalPool | false | 4,187 | [
"Apache-2.0"
] | 0 | c4e8a5be0ea6a8d93ea7cfd3a5bdab0560c50848 | https://github.com/praesc/end-to-end-SLU/tree/c4e8a5be0ea6a8d93ea7cfd3a5bdab0560c50848 |
WordPredictor | # 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.... | gardenia22/translate | WordPredictor | false | 6,745 | [
"BSD-3-Clause"
] | 1 | 0be57c8f55b52fc9d39197efa02e05d1c1cda024 | https://github.com/gardenia22/translate/tree/0be57c8f55b52fc9d39197efa02e05d1c1cda024 |
Model | from torch.nn import Module
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
from torch.nn import Parameter
from torch.nn import Module
class Model(Module):
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.nn import Module
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.utils.data
import torc... | FDecaYed/apex | Model | false | 4,753 | [
"BSD-3-Clause"
] | 0 | 789afd89fe2c5a3e772f557055a9cf0f5e9d1241 | https://github.com/FDecaYed/apex/tree/789afd89fe2c5a3e772f557055a9cf0f5e9d1241 |
ResidualAttentionBlock | # 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.... | johnpaulbin/glide-text2im | ResidualAttentionBlock | false | 12,646 | [
"MIT"
] | 0 | 4897050c4c540316dfb1ec7e6ff95698bcb20487 | https://github.com/johnpaulbin/glide-text2im/tree/4897050c4c540316dfb1ec7e6ff95698bcb20487 |
TransformerDecoderLayer | import torch
from torch import Tensor
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | johnjosephmorgan/snowfall | TransformerDecoderLayer | false | 12,756 | [
"Apache-2.0"
] | 0 | 604d789c0aed035626d6745e6d7a427168063cae | https://github.com/johnjosephmorgan/snowfall/tree/604d789c0aed035626d6745e6d7a427168063cae |
ArcFaceLoss | import math
import torch
from torch import nn
class DenseCrossEntropy(nn.Module):
""" The CrossEntropy loss that takes the one-hot
vector of the gt label as the input, should be equivalent to the
standard CrossEntropy implementation. The one-hot vector
is meant for the ArcFaceLoss and CutMix augmenta... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | CTPLab/IID_representation_learning | ArcFaceLoss | false | 4,951 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
L2Conv2D | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.utils.data
class L2Conv2D(nn.Module):
"""
Convolutional layer that computes the squared L2 distance instead of the conventional inner product.
"""
def __init__(self, num_prototypes, num_features, w_1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | M-Nauta/ProtoTree | L2Conv2D | false | 8,517 | [
"MIT"
] | 35 | 72cad5e42b0eb05c1312e5496f36b842726e081a | https://github.com/M-Nauta/ProtoTree/tree/72cad5e42b0eb05c1312e5496f36b842726e081a |
scaleCompositor | import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, in_ch, hid_ch):
super(ResBlock, self).__init__()
self.act = nn.ReLU()
self.conv1 = nn.Conv2d(in_ch, hid_ch, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(hid_ch, hid_ch, kernel_size=3, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | qbhan/pathembed | scaleCompositor | false | 7,533 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
MiniBatchStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
import torch.utils.cpp_extension
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
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.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | bladesaber/mmgeneration | MiniBatchStddevLayer | false | 1,567 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, factor):
super(Upsample, self).__init__()
self.factor = factor
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=self.factor, mode=
'bilinear', align_corners=False)
... | 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... | a3ahmad/DDPM | Upsample | false | 1,341 | [
"MIT"
] | 0 | 180440740cb82c2b4e7e0b06a0d8e662b5aa3f05 | https://github.com/a3ahmad/DDPM/tree/180440740cb82c2b4e7e0b06a0d8e662b5aa3f05 |
UpConv_Blocks | # 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_... | LuigiFilippoChiara/GoalGAN | UpConv_Blocks | false | 8,483 | [
"MIT"
] | 36 | 11ac7448af7ac8934e6eb47a06c51d92f04dec8c | https://github.com/LuigiFilippoChiara/GoalGAN/tree/11ac7448af7ac8934e6eb47a06c51d92f04dec8c |
SpatialAttention | # 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_... | Ming-er/NeuralNILM_Pytorch | SpatialAttention | false | 8,572 | [
"MIT"
] | 22 | 90123a3cf7d8dedc7f513ff784a45f178aa10a9d | https://github.com/Ming-er/NeuralNILM_Pytorch/tree/90123a3cf7d8dedc7f513ff784a45f178aa10a9d |
fpn_module | # 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 ... | ShenZheng2000/Syn2Real-Pytorch | fpn_module | false | 6,012 | [
"MIT"
] | 1 | 214c800914e2bcd57d4ca74a4c8476a11e1b5905 | https://github.com/ShenZheng2000/Syn2Real-Pytorch/tree/214c800914e2bcd57d4ca74a4c8476a11e1b5905 |
Foo | # 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.jit
import torch... | opti-mix/glow | Foo | false | 7,403 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
StableBCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | kevinkwshin/kaggle-pneumothorax | StableBCELoss | false | 15,805 | [
"MIT"
] | 74 | 24b91a9425097023f0cc7781a9380cb247babe22 | https://github.com/kevinkwshin/kaggle-pneumothorax/tree/24b91a9425097023f0cc7781a9380cb247babe22 |
ArcFaceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | aaron276h/kaggle-rcic-1st | ArcFaceLoss | false | 12,040 | [
"MIT"
] | 0 | d35e97847df3c29f548e60bc936d3fec7a0a4c08 | https://github.com/aaron276h/kaggle-rcic-1st/tree/d35e97847df3c29f548e60bc936d3fec7a0a4c08 |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | uthree/gan-image-generator | ToRGB | false | 11,000 | [
"MIT"
] | 0 | 85585e389b5a494393da0789d82824f8c811e263 | https://github.com/uthree/gan-image-generator/tree/85585e389b5a494393da0789d82824f8c811e263 |
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.... | marcoleewow/LaTeX_OCR | Attention | false | 16,017 | [
"Apache-2.0"
] | 290 | 0980ea719f8d3175a6bbf6af18873dd72d04b8c7 | https://github.com/marcoleewow/LaTeX_OCR/tree/0980ea719f8d3175a6bbf6af18873dd72d04b8c7 |
Conv2dBlock | # 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.functional as... | belphegor2211/KLTN_GANwriting | Conv2dBlock | false | 3,205 | [
"MIT"
] | 0 | 67d4d5c286ec45ef704b49c5abf9774d38bf65eb | https://github.com/belphegor2211/KLTN_GANwriting/tree/67d4d5c286ec45ef704b49c5abf9774d38bf65eb |
WassersteinGANLossDiscriminator | import torch
import torch.nn as nn
class WassersteinGANLossDiscriminator(nn.Module):
"""
This class implements the Wasserstein generator GAN loss proposed in:
http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf
"""
def __init__(self) ->None:
"""
Constructor method.
... | 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/Mode_Collapse | WassersteinGANLossDiscriminator | false | 7,905 | [
"MIT"
] | 14 | 937ee8bf96510fbf4070fc7e14b78276ab036b8c | https://github.com/ChristophReich1996/Mode_Collapse/tree/937ee8bf96510fbf4070fc7e14b78276ab036b8c |
Fusion | import torch
import torch.nn as nn
class Fusion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(Fusion, self).__init__()
self.linear = nn.Linear(input_dim * 4, hidden_dim, bias=True)
self.tanh = nn.Tanh()
def forward(self, x, y):
z = torch.cat([x, y, x * y, x - y... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | hgrhgy/NumSeq2SQL | Fusion | false | 10,223 | [
"MIT"
] | 0 | 6f22fdf108736f979afa2dbd3af14aa9ad4718aa | https://github.com/hgrhgy/NumSeq2SQL/tree/6f22fdf108736f979afa2dbd3af14aa9ad4718aa |
LayerNorm | # 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
from torch.nn.parameter import Parameter
from torch.optim... | aerinkim/squad_2018 | LayerNorm | false | 3,098 | [
"BSD-3-Clause"
] | 0 | 4479fa7ce92d8ab2f2eeb1823991d416924d8561 | https://github.com/aerinkim/squad_2018/tree/4479fa7ce92d8ab2f2eeb1823991d416924d8561 |
UNet | import torch
class Block(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
self.conv2 = torch.nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | amrane99/lung-segmentation | UNet | false | 12,162 | [
"MIT"
] | 0 | ab29db75ac78918da5cbf66b830acaf36cf7b44a | https://github.com/amrane99/lung-segmentation/tree/ab29db75ac78918da5cbf66b830acaf36cf7b44a |
CustomizedNet | import torch
import torch.nn as nn
import torch.utils.data.distributed
class CustomizedNet(nn.Module):
def __init__(self, dropout, input_size, input_feature_num, hidden_dim,
output_size):
"""
Simply use linear layers for multi-variate single-step forecasting.
"""
super()._... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | EvelynQiang/analytics-zoo | CustomizedNet | false | 11,408 | [
"Apache-2.0"
] | 0 | be5dd08abe9b14ac085817decd017862a273985a | https://github.com/EvelynQiang/analytics-zoo/tree/be5dd08abe9b14ac085817decd017862a273985a |
VitMlpHead | import torch
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True, help=
'Path to input JSON')
group.add_argument('--json-keys', nargs='+', default=['text'], help=
'space separate ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | parsa-epfl/Megatron-LM | VitMlpHead | false | 12,871 | [
"MIT"
] | 0 | 0301c00ce60b7c75f315e7aa4ff38238186762b1 | https://github.com/parsa-epfl/Megatron-LM/tree/0301c00ce60b7c75f315e7aa4ff38238186762b1 |
DecoderLayer | # 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.... | sd2001/seqModeling | DecoderLayer | false | 13,003 | [
"MIT"
] | 0 | 393f680de711ea8477e5450633b492298d253368 | https://github.com/sd2001/seqModeling/tree/393f680de711ea8477e5450633b492298d253368 |
StyleMod | # 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... | eitanrich/ganspace-manifold | StyleMod | false | 12,338 | [
"Apache-2.0"
] | 0 | 148d5d30001c43794a40bbed885601e7816f5d7d | https://github.com/eitanrich/ganspace-manifold/tree/148d5d30001c43794a40bbed885601e7816f5d7d |
SoftmaxLoss | import torch
import torch.nn as nn
class SoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, **kwargs):
"""
Softmax Loss
"""
super(SoftmaxLoss, self).__init__()
self.fc = nn.Linear(hidden_dim, speaker_num)
self.loss = nn.CrossEntropyLoss()
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.... | czlwang/s3prl | SoftmaxLoss | false | 12,276 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
SelfAttnPooler | # 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.... | ankitapasad/slue-toolkit | SelfAttnPooler | false | 12,096 | [
"MIT"
] | 0 | db8155cf0fc803e21890cf4eee2ef87152aafbfc | https://github.com/ankitapasad/slue-toolkit/tree/db8155cf0fc803e21890cf4eee2ef87152aafbfc |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sy-Zhang/recurrent-transformer | BertSelfAttention | false | 11,141 | [
"MIT"
] | 0 | f66ba49a2c9ec42759d3d00d497b49ffe39e18de | https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de |
BertSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shrishabh/cs769-assignments | BertSelfAttention | false | 12,992 | [
"MIT"
] | 0 | babce1def0d65728bf1d4e4a725d8939f1d5f9a7 | https://github.com/shrishabh/cs769-assignments/tree/babce1def0d65728bf1d4e4a725d8939f1d5f9a7 |
Scale | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AmorosTech/RP-R-CNN | Scale | false | 13,251 | [
"MIT"
] | 78 | 45557a69ae9789e2662e3b937feb7624319a3e73 | https://github.com/AmorosTech/RP-R-CNN/tree/45557a69ae9789e2662e3b937feb7624319a3e73 |
TransformerSet | import torch
from torch import nn
class TransformerSet(nn.Module):
def __init__(self, input_size, dropout=0.5, trans_head_nums=1, **kwargs):
super(TransformerSet, self).__init__()
self.Transformer = nn.MultiheadAttention(embed_dim=input_size,
num_heads=trans_head_nums, dropout=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.... | Asichurter/MalFusionFSL | TransformerSet | false | 16,992 | [
"MIT"
] | 4 | 713bf64cc07a3489f42941fd2299837075575ac0 | https://github.com/Asichurter/MalFusionFSL/tree/713bf64cc07a3489f42941fd2299837075575ac0 |
Connection_Combination | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class Connection_Combination(nn.Module):
"""combine 3 types of connection method by 'beta' weights to become an input node """
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | senyang-ml/PoseNFS | Connection_Combination | false | 16,380 | [
"MIT"
] | 53 | 1229abb69917dab1e57def3de0e3fe9a8a3164cd | https://github.com/senyang-ml/PoseNFS/tree/1229abb69917dab1e57def3de0e3fe9a8a3164cd |
ResidualBlock | import torch
import torch.nn as nn
from functools import partial
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
def ncsn_conv3x3(in_planes, out_pla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DeepTitan/PNDM | ResidualBlock | false | 13,577 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
Wav2Vec2ClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Wav2Vec2ClassificationHead(nn.Module):
"""Head for wav2vec classification task."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Ayushk4/MedImaging | Wav2Vec2ClassificationHead | false | 1,871 | [
"MIT"
] | 0 | dbc8968f076385be0c8db42210817ae0940fa26a | https://github.com/Ayushk4/MedImaging/tree/dbc8968f076385be0c8db42210817ae0940fa26a |
ConvolutionBlock | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Seungwoo0326/WaveGrad2-1 | ConvolutionBlock | false | 14,450 | [
"MIT"
] | 45 | 3b202201348449b89353f28bce1596ca7939a810 | https://github.com/Seungwoo0326/WaveGrad2-1/tree/3b202201348449b89353f28bce1596ca7939a810 |
DecayModule | import math
import torch
import torch.nn as nn
class DecayModule(nn.Module):
def __init__(self, input_size, hidden_size, bias=True, num_chunks=1,
activation='relu', nodiag=False):
super(DecayModule, self).__init__()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Hritikbansal/RNNs_SVA_OOD | DecayModule | false | 17,399 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 |
L1_Charbonnier_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | ankurbhatia24/image-super-resolution | L1_Charbonnier_loss | false | 9,752 | [
"Apache-2.0"
] | 0 | 7ebc2be70e1a940addb6ba886a663f88167e6007 | https://github.com/ankurbhatia24/image-super-resolution/tree/7ebc2be70e1a940addb6ba886a663f88167e6007 |
GeneralizedMeanPoolingList | import torch
from abc import ABC
from torch import nn
class GeneralizedMeanPoolingList(nn.Module, ABC):
"""Applies a 2D power-average adaptive pooling over an input signal composed of
several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one 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 import triton_helpers
from abc import ABC
from torch import nn
assert_size_stride = torch._C._dynamo.guards.ass... | catcodee/cluster-contrast-reid | GeneralizedMeanPoolingList | false | 3,297 | [
"MIT"
] | 0 | f6359990a4326375f23c3fd654df3fc6dcc9c579 | https://github.com/catcodee/cluster-contrast-reid/tree/f6359990a4326375f23c3fd654df3fc6dcc9c579 |
distLinear | import torch
import torch.nn as nn
from torch.nn.utils.weight_norm import WeightNorm
class distLinear(nn.Module):
def __init__(self, indim, outdim):
super(distLinear, self).__init__()
self.L = nn.Linear(indim, outdim, bias=False)
self.class_wise_learnable_norm = True
if self.class... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | horsepurve/DeepVoro | distLinear | false | 3,627 | [
"MIT"
] | 0 | 1b67a8e0d51e1c966a2af96d4b6a495f8390f608 | https://github.com/horsepurve/DeepVoro/tree/1b67a8e0d51e1c966a2af96d4b6a495f8390f608 |
FeedForward | # 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.... | mcx/annotated_deep_learning_paper_implementations | FeedForward | false | 7,208 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
BatchSpectralPenalizationLoss | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | neka-nat/Transfer-Learning-Library | BatchSpectralPenalizationLoss | false | 16,137 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
MinibatchStdLayer | import torch
from torch import nn
class MinibatchStdLayer(nn.Module):
def __init__(self, group_size=4):
super().__init__()
self.group_size = group_size
def forward(self, x):
group_size = min(self.group_size, x.shape[0])
s = x.shape
y = x.view([group_size, -1, s[1], 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.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | BillyXYB/TransEditor | MinibatchStdLayer | false | 17,069 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn
import torch.nn as nn
class BertOutput(nn.Module):
"""BERT output layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
imp... | Project-MONAI/MONAI | BertOutput | false | 16,225 | [
"Apache-2.0"
] | 2,971 | 2bab12c67c3cc1d54a4847628ce1e879064be11c | https://github.com/Project-MONAI/MONAI/tree/2bab12c67c3cc1d54a4847628ce1e879064be11c |
GELU | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function:
.. math:: ext{GELU}(x) = x * \\Phi(x)
where :math:`\\Phi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Crazy-Jack/SpatialExpGeneCluster | GELU | false | 315 | [
"MIT"
] | 0 | 9e57c308d1c577a936a2358d0641c65b8130034f | https://github.com/Crazy-Jack/SpatialExpGeneCluster/tree/9e57c308d1c577a936a2358d0641c65b8130034f |
CoreKernelTensorRing | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn import Parameter
assert_size_stri... | AndresOtero/TensorDecompositionMachineLearning | CoreKernelTensorRing | false | 16,917 | [
"MIT"
] | 3 | 455f16b405ec9d031999b0ebf9c5a68d3c20b233 | https://github.com/AndresOtero/TensorDecompositionMachineLearning/tree/455f16b405ec9d031999b0ebf9c5a68d3c20b233 |
GAT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | iaongstudio/PaperRobot | GAT | false | 3,674 | [
"MIT"
] | 0 | d7d2a87822e1fb473e5c72ffc6b83d1022ecd3c1 | https://github.com/iaongstudio/PaperRobot/tree/d7d2a87822e1fb473e5c72ffc6b83d1022ecd3c1 |
NormalAttention_gaussian | import torch
import torch.nn as nn
class NormalAttention_gaussian(nn.Module):
def __init__(self, input_channel_num):
super(NormalAttention_gaussian, self).__init__()
self.c_in = input_channel_num
self.value_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
self.c_in, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Schwartz-Zha/My-invertible-resnet | NormalAttention_gaussian | false | 1,030 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
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.... | baduncan/Pytorch-seq2seq-Beam-Search | Attention | false | 12,148 | [
"MIT"
] | 0 | 82e2f12563d4db520a9a9089e7205f398ca53699 | https://github.com/baduncan/Pytorch-seq2seq-Beam-Search/tree/82e2f12563d4db520a9a9089e7205f398ca53699 |
ConvNet | import torch
import torch.nn as nn
import torch.nn.functional as tF
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 5, 3, 1)
self.conv2 = nn.Conv2d(5, 6, 4, 1, bias=False)
self.conv3 = nn.Conv2d(6, 7, 3, 1)
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
from torch._inductor.runtime.... | amyami187/nngeometry | ConvNet | false | 14,848 | [
"MIT"
] | 103 | cb516da3f7a019e148f48ff3ef3bed0cdae0d184 | https://github.com/amyami187/nngeometry/tree/cb516da3f7a019e148f48ff3ef3bed0cdae0d184 |
DrugDrugAttentionLayer | import torch
import torch.nn.functional
class DrugDrugAttentionLayer(torch.nn.Module):
"""Co-attention layer for drug pairs."""
def __init__(self, feature_number: 'int'):
"""Initialize the co-attention layer.
:param feature_number: Number of input features.
"""
super().__init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | YuWVandy/chemicalx | DrugDrugAttentionLayer | false | 1,275 | [
"Apache-2.0"
] | 0 | c02f979a502409c26700e6d5a1b2e6c0aa77e64c | https://github.com/YuWVandy/chemicalx/tree/c02f979a502409c26700e6d5a1b2e6c0aa77e64c |
MP | from torch.nn import Module
import torch
import torch.utils.data
from torch.nn import MaxPool2d
class MP(Module):
def __init__(self, k=2):
super().__init__()
self.m = MaxPool2d(kernel_size=k, stride=k)
def forward(self, x):
return self.m(x)
def get_inputs():
return [torch.rand(... | 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
import torch.utils.data
from torch.nn import MaxPool2d
assert... | ioangatop/yolo | MP | false | 6,892 | [
"MIT"
] | 1 | c65a72337369572bc07090f39123e2bf6ff5f4a3 | https://github.com/ioangatop/yolo/tree/c65a72337369572bc07090f39123e2bf6ff5f4a3 |
L2 | import torch
import torch.nn as nn
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([... | 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_... | B06901052/deep-stabilization | L2 | false | 97 | [
"Apache-2.0"
] | 0 | b6030b463cf1f1128660e900669f43e742aa2651 | https://github.com/B06901052/deep-stabilization/tree/b6030b463cf1f1128660e900669f43e742aa2651 |
ELU | import torch
import torch.nn as nn
class ActivationFunction(nn.Module):
def __init__(self):
super().__init__()
self.name = self.__class__.__name__
self.config = {'name': self.name}
class ELU(ActivationFunction):
def forward(self, x):
return torch.where(x > 0, x, torch.exp(x... | 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... | jiwidi/lightning-tutorials | ELU | false | 15,695 | [
"Apache-2.0"
] | 114 | 70ba437447f345d4d6ba089d5b30fd1da2cbc04b | https://github.com/jiwidi/lightning-tutorials/tree/70ba437447f345d4d6ba089d5b30fd1da2cbc04b |
DuelingNetwork | import torch
import torch.nn as nn
class DuelingNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(DuelingNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
self.action_size = action_size
self.fc1 = nn.Linear(state_size, 64)
self.relu1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bluebibi/rl_book_codes | DuelingNetwork | false | 3,226 | [
"MIT"
] | 0 | ef7fc9993eb66618e4b4e80e59cc2879a8db3522 | https://github.com/bluebibi/rl_book_codes/tree/ef7fc9993eb66618e4b4e80e59cc2879a8db3522 |
SplitAndConcat | import torch
import torch.nn as nn
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concatenated
@param chunk size of the da... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | sstsai-adl/d2go | SplitAndConcat | false | 16,488 | [
"Apache-2.0"
] | 687 | 6cff773797b14698043589afe57ea67cd76286f9 | https://github.com/sstsai-adl/d2go/tree/6cff773797b14698043589afe57ea67cd76286f9 |
AttentivePooling | # 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.... | AyushExel/s3prl | AttentivePooling | false | 1,985 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
BCEAfterSigmoidLoss | import torch
from torch import nn
from torch.nn import functional
import torch.autograd
class Loss(nn.Module):
"""A loss function."""
class PointwiseLoss(Loss):
"""Pointwise loss functions compute an independent loss term for each triple-label pair."""
class BCEAfterSigmoidLoss(PointwiseLoss):
"""A lo... | 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 ... | johnbachman/pykeen | BCEAfterSigmoidLoss | false | 3,761 | [
"MIT"
] | 0 | 6595f6cefc462b6d1e057446e6c3ed66d36a078b | https://github.com/johnbachman/pykeen/tree/6595f6cefc462b6d1e057446e6c3ed66d36a078b |
Critic | # 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 ... | LQNew/AUMC | Critic | false | 17,744 | [
"MIT"
] | 5 | c3ce9c289bc8c0912431d68ec4fe260f640df3bc | https://github.com/LQNew/AUMC/tree/c3ce9c289bc8c0912431d68ec4fe260f640df3bc |
Block | import torch
import torch.nn as nn
import torch.nn.functional as F
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 with
shape (batch_size, height, width, c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AnweshCR7/convNeXt | Block | false | 8,868 | [
"MIT"
] | 0 | 5400dd0f7c793f497057f5548b49e3969a540504 | https://github.com/AnweshCR7/convNeXt/tree/5400dd0f7c793f497057f5548b49e3969a540504 |
ChannelAttentionModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LeftAttention/Attention-Codebase | ChannelAttentionModule | false | 17,596 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
ConvLSTMCell | import torch
import torch.nn as nn
from torch.autograd import Variable
class ConvLSTMCell(nn.Module):
def __init__(self, input_channels, hidden_channels, kernel_size, bias=True
):
super(ConvLSTMCell, self).__init__()
assert hidden_channels % 2 == 0
self.input_channels = input_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Kwanss/PCLNet | ConvLSTMCell | false | 8,402 | [
"MIT"
] | 31 | d288820975a9daf23eab47c52d7ea6f7dd564725 | https://github.com/Kwanss/PCLNet/tree/d288820975a9daf23eab47c52d7ea6f7dd564725 |
TrajectoryPredictor | # 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... | CMU-MultiComp-Lab/language2pose | TrajectoryPredictor | false | 4,931 | [
"MIT"
] | 1 | b32199ae5b2b80087411504afef384e0fa689d04 | https://github.com/CMU-MultiComp-Lab/language2pose/tree/b32199ae5b2b80087411504afef384e0fa689d04 |
ConstMult | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | RaoefTaki/MNTDP-forked | ConstMult | false | 8,686 | [
"MIT"
] | 15 | d9ea59a6638f6cdc93eca180ab02672f5bf5d2a1 | https://github.com/RaoefTaki/MNTDP-forked/tree/d9ea59a6638f6cdc93eca180ab02672f5bf5d2a1 |
Decoder2 | import torch
import torch.nn as nn
class Decoder2(nn.Module):
def __init__(self, model=None, fixed=False):
super(Decoder2, self).__init__()
self.fixed = fixed
self.conv21 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv12 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1)
self.conv11 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MingSun-Tse/Collaborative-Distillation | Decoder2 | false | 14,021 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
LayerNorm | # 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_... | ChavesLiu/pytorch-dc-tts | LayerNorm | false | 13,461 | [
"MIT"
] | 145 | 29a1ab11f69b2c4316ae0a8766e995b96385a29f | https://github.com/ChavesLiu/pytorch-dc-tts/tree/29a1ab11f69b2c4316ae0a8766e995b96385a29f |
HardSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.funct... | bcaitech1/p4-mod-model_diet | HardSwish | false | 6,314 | [
"MIT"
] | 1 | 36d8a747e12c375b07d132ed4d08f9fc77126a8b | https://github.com/bcaitech1/p4-mod-model_diet/tree/36d8a747e12c375b07d132ed4d08f9fc77126a8b |
SRCNN | import torch
from torchvision.transforms import *
import torch.nn as nn
class SRCNN(nn.Module):
def __init__(self):
super(SRCNN, self).__init__()
self.input = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=
9, padding=9 // 2)
self.conv = nn.Conv2d(in_channels=64, out_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms i... | FYLSunghwan/VDSR-pytorch | SRCNN | false | 2,641 | [
"MIT"
] | 0 | fb862e97756078db2d5def095d46cc22a07cd014 | https://github.com/FYLSunghwan/VDSR-pytorch/tree/fb862e97756078db2d5def095d46cc22a07cd014 |
CosLoss | import torch
from torch import nn
import torch.utils.data
class CosLoss(nn.Module):
def __init__(self, factor=6e-07, havesum=True, havemax=True):
super(CosLoss, self).__init__()
self.factor = factor
self.havesum = havesum
self.havemax = havemax
def forward(self, w):
m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | PatrickGui/Face_Pytorch | CosLoss | false | 971 | [
"Apache-2.0"
] | 0 | ff5b820ca3978883f7cf95f0209fba3ee958c939 | https://github.com/PatrickGui/Face_Pytorch/tree/ff5b820ca3978883f7cf95f0209fba3ee958c939 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Geoffrey1500/mmsegmentation | DiceLoss | false | 11,458 | [
"Apache-2.0"
] | 0 | 0a5544c46e6ea1e07ed47858d5fcb39a5ae974b1 | https://github.com/Geoffrey1500/mmsegmentation/tree/0a5544c46e6ea1e07ed47858d5fcb39a5ae974b1 |
Dense | from torch.autograd import Function
from torch.nn import Module
import torch
from torch.nn import Parameter
class DenseFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
output = input.mm(weight.t())
if bias is not None:
output += bias.unsqueeze(0).expan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
from torch.nn import Module
from torch.nn im... | tczhangzhi/pytorch-parallel | Dense | false | 16,535 | [
"MIT"
] | 117 | 8d8baf80dd48234386051d0bab616de5b55f8f5c | https://github.com/tczhangzhi/pytorch-parallel/tree/8d8baf80dd48234386051d0bab616de5b55f8f5c |
SpatialDepthWiseConvolution | from torch.nn import Module
import math
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWiseConvolution(Module):
"""
## Spatial Depth Wise Convolution
This is actually slower
"""
def __init__(self, d_k: 'int', kernel_si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWiseConvolution | false | 16,572 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
ConvDownsample | import torch
from torch import nn
class ConvDownsample(nn.Module):
"""Convolutional Downsampling of ConvMLP."""
def __init__(self, embed_dim_in, embed_dim_out):
super().__init__()
self.downsample = nn.Conv2d(embed_dim_in, embed_dim_out, 3, stride=
2, padding=1)
def forward(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Jack-Hu-2001/UniverseNet | ConvDownsample | false | 13,862 | [
"Apache-2.0"
] | 314 | 03e7b8442286f951c65fe730ec86b9441005ac1b | https://github.com/Jack-Hu-2001/UniverseNet/tree/03e7b8442286f951c65fe730ec86b9441005ac1b |
KLDLossWithStandardGaussian | import torch
import torch.nn as nn
import torch.utils.data
class KLDLossWithStandardGaussian(nn.Module):
def forward(self, mu, logvar):
return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | atmacvit/meronymnet | KLDLossWithStandardGaussian | false | 6,267 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
InformedSender | # 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.... | Daetheys/Lazimpa | InformedSender | false | 7,956 | [
"MIT"
] | 15 | 21f4f4ebcdfa8b6a775b64673dd3001763c91cf1 | https://github.com/Daetheys/Lazimpa/tree/21f4f4ebcdfa8b6a775b64673dd3001763c91cf1 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, features, epsilon=1e-06):
super(Normalize, self).__init__()
self.gain = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.epsilon = epsilon
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | jingraham/struct2seq | Normalize | false | 15,691 | [
"MIT"
] | 106 | 22e497a2b565fe82f17e12ea37e89dcf4e50e92f | https://github.com/jingraham/struct2seq/tree/22e497a2b565fe82f17e12ea37e89dcf4e50e92f |
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