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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
from torch import nn
assert_s... | lulor/project_vg | SpatialAttention | false | 7,129 | [
"MIT"
] | 1 | 27b0c3b3038c5a666dde516a0a265ae8ddf2059f | https://github.com/lulor/project_vg/tree/27b0c3b3038c5a666dde516a0a265ae8ddf2059f |
TransformerDecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | marcinwitkowski/icefall | TransformerDecoderLayer | false | 10,501 | [
"Apache-2.0"
] | 0 | 73e917f689fa2ebfcfe5d484a34a262e74b77581 | https://github.com/marcinwitkowski/icefall/tree/73e917f689fa2ebfcfe5d484a34a262e74b77581 |
PositionwiseFeedForward | import math
import torch
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LogIntelligence/LogADEmpirical | PositionwiseFeedForward | false | 8,493 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
SplAtConv2d | # 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.... | CVI-SZU/CLIMS | SplAtConv2d | false | 17,080 | [
"MIT"
] | 4 | 9d3d0123b625b2c6941069e8fb359019a5cabd59 | https://github.com/CVI-SZU/CLIMS/tree/9d3d0123b625b2c6941069e8fb359019a5cabd59 |
NonAttentiveTacotronLoss | import torch
from torch import nn
import torch.nn.functional as F
class NonAttentiveTacotronLoss(nn.Module):
def __init__(self, sample_rate: 'int', hop_size: 'int'):
super(NonAttentiveTacotronLoss, self).__init__()
self.sample_rate = sample_rate
self.hop_size = hop_size
def forward(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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | IMDxD/NonAttentiveTacotron | NonAttentiveTacotronLoss | false | 17,420 | [
"MIT"
] | 4 | a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | https://github.com/IMDxD/NonAttentiveTacotron/tree/a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba |
CeCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | johnson7788/mt-dnn | CeCriterion | false | 3,888 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
Gaussian | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | chc273/torchani | Gaussian | false | 10,024 | [
"MIT"
] | 0 | bbcd7bedc254796f0c2f839c4868ac211ad9078d | https://github.com/chc273/torchani/tree/bbcd7bedc254796f0c2f839c4868ac211ad9078d |
Skew | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | Skew | false | 11,859 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
adaLIN | # 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
assert_size_stri... | ldzhangyu/photo2cartoon | adaLIN | false | 3,890 | [
"MIT"
] | 0 | d5b371e77e61018c28109db67e8306e5e6064800 | https://github.com/ldzhangyu/photo2cartoon/tree/d5b371e77e61018c28109db67e8306e5e6064800 |
BinaryChunk | import math
import torch
class BinaryChunk(torch.nn.Module):
def __init__(self, nCls, isLogit=False, pooling='max', chunk_dim=-1):
super(BinaryChunk, self).__init__()
self.nClass = nCls
self.nChunk = int(math.ceil(math.log2(self.nClass)))
self.pooling = pooling
self.isLogi... | 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | azopticsinc/optical-neural-network | BinaryChunk | false | 6,326 | [
"MIT"
] | 1 | 28280014a6c1fc717a5077ed5e3c3496a4b103ac | https://github.com/azopticsinc/optical-neural-network/tree/28280014a6c1fc717a5077ed5e3c3496a4b103ac |
ClampExp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | mbaddar1/normalizing-flows | ClampExp | false | 10,770 | [
"MIT"
] | 0 | d1409464a65234354b29ed9ea0ede2d12100440c | https://github.com/mbaddar1/normalizing-flows/tree/d1409464a65234354b29ed9ea0ede2d12100440c |
Mlayer | # 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... | Sharingsky/resrep | Mlayer | false | 9,496 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
CNNCOVID19 | # 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.... | NaiboWang/HFL-CS6203-NaiboShiqi | CNNCOVID19 | false | 11,133 | [
"MIT"
] | 0 | 4bab35a20f1ec1229b0011c952d93c341579c402 | https://github.com/NaiboWang/HFL-CS6203-NaiboShiqi/tree/4bab35a20f1ec1229b0011c952d93c341579c402 |
BCEBlurWithLogitsLoss | # 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... | AsakusaRinne/tensorrt_yolov5_tracker | BCEBlurWithLogitsLoss | false | 7,734 | [
"MIT"
] | 22 | b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 | https://github.com/AsakusaRinne/tensorrt_yolov5_tracker/tree/b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 |
TripletAllLoss | import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.model_zoo
def pdist(A, squared=False, eps=0.0001):
prod = torch.mm(A, A.t())
norm = prod.diag().unsqueeze(1).expand_as(prod)
res = (norm + norm.t() - 2 * prod).clamp(min=0)
if squared:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CompVis/metric-learning-divide-and-conquer-improved | TripletAllLoss | false | 8,200 | [
"MIT"
] | 11 | 33fe768a54376a090e2d7139898177b06e8903d2 | https://github.com/CompVis/metric-learning-divide-and-conquer-improved/tree/33fe768a54376a090e2d7139898177b06e8903d2 |
FCN8VGG16 | import torch
import numpy as np
from torch import nn
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1, padding=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(
stride, stride), padding=(padding, padding))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | alzayats/DeepFish | FCN8VGG16 | false | 15,065 | [
"MIT"
] | 48 | 4d9ebfb0474a7e9346c72e2a5411ab6f72e878e2 | https://github.com/alzayats/DeepFish/tree/4d9ebfb0474a7e9346c72e2a5411ab6f72e878e2 |
AttentionPool2d | import math
import torch
import numpy as np
import torch as th
import torch.nn as nn
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ZGCTroy/guided-diffusion | AttentionPool2d | false | 1,307 | [
"MIT"
] | 0 | af987bb2b65db2875148a5466df79736ea5ae6a1 | https://github.com/ZGCTroy/guided-diffusion/tree/af987bb2b65db2875148a5466df79736ea5ae6a1 |
ScaledDotProductAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
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_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | awesome-archive/FEAT | ScaledDotProductAttention | false | 6,291 | [
"MIT"
] | 1 | 940d525fcbf2a40528d284392a03e4b0193344a7 | https://github.com/awesome-archive/FEAT/tree/940d525fcbf2a40528d284392a03e4b0193344a7 |
Spatial_Attention_layer | import torch
import torch.nn.functional as F
import torch.nn as nn
class Spatial_Attention_layer(nn.Module):
"""
compute spatial attention scores
"""
def __init__(self, DEVICE, in_channels, num_of_vertices, num_of_timesteps):
super(Spatial_Attention_layer, self).__init__()
self.W1 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | msalvato/pytorch_geometric_temporal | Spatial_Attention_layer | false | 10,594 | [
"MIT"
] | 0 | 149bd46d3b2bddfc3570e31a91a3f53e8873d50e | https://github.com/msalvato/pytorch_geometric_temporal/tree/149bd46d3b2bddfc3570e31a91a3f53e8873d50e |
ResHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn as nn
import torch.utils.data
assert... | MAC-AutoML/XCompression | ResHead | false | 5,558 | [
"MIT"
] | 1 | 9f76eb3ccfb3057110ecf12aa48dec00a4667a25 | https://github.com/MAC-AutoML/XCompression/tree/9f76eb3ccfb3057110ecf12aa48dec00a4667a25 |
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from typing import Tuple
imp... | VincentSch4rf/torchtime | Normalize | false | 18,044 | [
"Apache-2.0"
] | 4 | bebd006cd67b31c342e0658285c9771c27411df0 | https://github.com/VincentSch4rf/torchtime/tree/bebd006cd67b31c342e0658285c9771c27411df0 |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Shawn-Guo-CN/EGG | InformedSender | false | 2,882 | [
"MIT"
] | 0 | 0a5b258108e2cd1c873d7f67e8c92551bb3d809c | https://github.com/Shawn-Guo-CN/EGG/tree/0a5b258108e2cd1c873d7f67e8c92551bb3d809c |
ClassificationModel | import torch
import torch.nn as nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=21, num_classes=15,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HenryOsborne/Rotation | ClassificationModel | false | 9,126 | [
"Apache-2.0"
] | 0 | 417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | https://github.com/HenryOsborne/Rotation/tree/417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 |
SphericalBesselBasis | import math
import torch
import numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int'... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | Irlirion/ocp | SphericalBesselBasis | false | 13,843 | [
"MIT",
"BSD-3-Clause"
] | 242 | 6fb3e794eef31559db990300198eca20f41d8f37 | https://github.com/Irlirion/ocp/tree/6fb3e794eef31559db990300198eca20f41d8f37 |
IoU_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | debrouchovea/ReproduceGoturn | IoU_loss | false | 3,412 | [
"MIT"
] | 0 | d60f13c781ca612cacc17536530bbee989bdfa45 | https://github.com/debrouchovea/ReproduceGoturn/tree/d60f13c781ca612cacc17536530bbee989bdfa45 |
CombineSlices | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | gbosdet/fastMRI | CombineSlices | false | 6,716 | [
"MIT"
] | 1 | 7f94f8006f8919d98fb87788b6dadec9a58d1a3a | https://github.com/gbosdet/fastMRI/tree/7f94f8006f8919d98fb87788b6dadec9a58d1a3a |
SP | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | ijinjay/torch2mindspore | SP | false | 3,658 | [
"MIT"
] | 0 | e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 | https://github.com/ijinjay/torch2mindspore/tree/e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 |
Affine | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
def forward(self, 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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Yuki-Tanaka-33937424/pytorch-image-models | Affine | false | 12,027 | [
"Apache-2.0"
] | 0 | 6c1da622dcb2a0421aeb6cdcadd03cc366331f66 | https://github.com/Yuki-Tanaka-33937424/pytorch-image-models/tree/6c1da622dcb2a0421aeb6cdcadd03cc366331f66 |
L1Loss | # 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... | oskarnatan/RGBDVS-fusion | L1Loss | false | 7,423 | [
"MIT"
] | 1 | 5e560f54442d387a86e3a469107cf65859693987 | https://github.com/oskarnatan/RGBDVS-fusion/tree/5e560f54442d387a86e3a469107cf65859693987 |
SoftSelectPrototype | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiaweiSheng/FAAN | SoftSelectPrototype | false | 8,366 | [
"MIT"
] | 41 | b439b829506c4e2e9044a6b2ab7f3d844f445a95 | https://github.com/JiaweiSheng/FAAN/tree/b439b829506c4e2e9044a6b2ab7f3d844f445a95 |
ArcCosDotLoss | # 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
assert_size_stride = torch._... | acycliq/cellpose | ArcCosDotLoss | false | 12,046 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a |
GraphConv | # 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
import torch.nn
import torch.autograd
assert_size_stride = ... | shumash/kaolin | GraphConv | false | 12,980 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 2158b5ec7a28d57d7df7e606adbb0c693a0145f0 | https://github.com/shumash/kaolin/tree/2158b5ec7a28d57d7df7e606adbb0c693a0145f0 |
Fire | # 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... | GerardWalsh/DeepLabv3FineTuning | Fire | false | 11,548 | [
"MIT"
] | 0 | 149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | https://github.com/GerardWalsh/DeepLabv3FineTuning/tree/149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 |
PONO | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Warvito/lmconv | PONO | false | 14,560 | [
"MIT"
] | 69 | 01adba51e3fff1e7da99324dc64a9fc9cd38621e | https://github.com/Warvito/lmconv/tree/01adba51e3fff1e7da99324dc64a9fc9cd38621e |
FscoreMetric | import torch
import torch.nn as nn
def f_score(pr, gt, beta=1, eps=1e-07, threshold=0.5):
"""dice score(also referred to as F1-score)"""
if threshold is not None:
pr = (pr > threshold).float()
tp = torch.sum(gt * pr)
fp = torch.sum(pr) - tp
fn = torch.sum(gt) - tp
score = ((1 + beta **... | 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... | JACKYLUO1991/HybridNet | FscoreMetric | false | 17,454 | [
"Apache-2.0"
] | 6 | eb97d8a048ca4bb4087bc542360172e169a08dbf | https://github.com/JACKYLUO1991/HybridNet/tree/eb97d8a048ca4bb4087bc542360172e169a08dbf |
LabelSmoothCELoss | # 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
... | L-Net-1992/DI-engine | LabelSmoothCELoss | false | 5,494 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
LeNet | # 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... | AluminiumOxide/pytorch_base_-tutorial | LeNet | false | 1,946 | [
"Apache-2.0"
] | 0 | a6d3bea6070c7c774dcd7c55d94b0a1441548c8b | https://github.com/AluminiumOxide/pytorch_base_-tutorial/tree/a6d3bea6070c7c774dcd7c55d94b0a1441548c8b |
_BoundaryRefineModule | import torch
from torch import nn
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | CuthbertCai/pytorch-semantic-segmentation | _BoundaryRefineModule | false | 13,526 | [
"MIT"
] | 1,328 | aa2a47b73c1aa14555e1421e2366275254ea5376 | https://github.com/CuthbertCai/pytorch-semantic-segmentation/tree/aa2a47b73c1aa14555e1421e2366275254ea5376 |
MLP_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 torch.nn.functional as... | Aks-Dmv/maddpg-pytorch | MLP_Qnet | false | 4,828 | [
"MIT"
] | 1 | 8afe2448875824cf5aee69c5d0314a3e00777b6f | https://github.com/Aks-Dmv/maddpg-pytorch/tree/8afe2448875824cf5aee69c5d0314a3e00777b6f |
AE | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Linear
class AE(nn.Module):
def __init__(self, n_enc_1, n_enc_2, n_enc_3, n_dec_1, n_dec_2, n_dec_3,
n_input, n_z):
super(AE, self).__init__()
self.enc_1 = Linear(n_input, n_enc_1)
self.enc_2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | drzhang3/SDCN | AE | false | 15,244 | [
"Apache-2.0"
] | 146 | 3d11365bcb4af2cbe9625362737f1224aeea3b72 | https://github.com/drzhang3/SDCN/tree/3d11365bcb4af2cbe9625362737f1224aeea3b72 |
ResidualSequential | import torch
import torch.nn as nn
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None
if out.size(2) != x.siz... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DDQXZcp/FYP_ProjectFile_TANG_Zhiheng | ResidualSequential | false | 8,941 | [
"MIT"
] | 0 | b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d | https://github.com/DDQXZcp/FYP_ProjectFile_TANG_Zhiheng/tree/b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d |
IRHead | # 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 ... | zhongerqiandan/OpenDialog | IRHead | false | 16,819 | [
"MIT"
] | 98 | f478b2a912c8c742da5ced510ac40da59217ddb3 | https://github.com/zhongerqiandan/OpenDialog/tree/f478b2a912c8c742da5ced510ac40da59217ddb3 |
MeanStd | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jwen307/pytorch_GAN_zoo | MeanStd | false | 10,366 | [
"BSD-3-Clause"
] | 0 | b1e538a2f03fda42bd7a12872238b770ea5e0f23 | https://github.com/jwen307/pytorch_GAN_zoo/tree/b1e538a2f03fda42bd7a12872238b770ea5e0f23 |
MaskedTemporalPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.asse... | denred0/pytorchvideo | MaskedTemporalPooling | false | 1,834 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 |
DQNFeatureNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DQNFeatureNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[0]
self._h1 = nn.Conv2d(n_input, 32, kernel_size=8, stride=4)
self._h2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | jacarvalho/mushroom-rl-benchmark | DQNFeatureNetwork | false | 12,553 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
biLinearModel | import torch
import torch.distributed
import torch
import torch.nn as nn
class biLinearModel(nn.Module):
"""Currently just for a pair"""
def __init__(self, hidden_size):
super(biLinearModel, self).__init__()
self.bilinear = nn.Bilinear(hidden_size, hidden_size, 1)
def forward(self, doc_e... | 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.distributed
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | BoonthichaSaejia/ThaiSum | biLinearModel | false | 7,820 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
BertAttention | 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.... | cjinchao/mner | BertAttention | false | 1,738 | [
"MIT"
] | 0 | 12776280da314eb7ef22511aa18ca9af0764fb32 | https://github.com/cjinchao/mner/tree/12776280da314eb7ef22511aa18ca9af0764fb32 |
EquivariantLayer | # 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
import torch.nn a... | Manojbhat09/Sane-annotation-shape-complete | EquivariantLayer | false | 17,700 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
CircleLoss | import torch
from torch import Tensor
from torch import nn
class CircleLoss(nn.Module):
def __init__(self, m: 'float', gamma: 'float') ->None:
super(CircleLoss, self).__init__()
self.m = m
self.gamma = gamma
self.soft_plus = nn.Softplus()
def forward(self, sp: 'Tensor', sn: '... | 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 ... | kagawa123/Person_reID_baseline_pytorch | CircleLoss | false | 10,363 | [
"MIT"
] | 0 | a503af2fa329406e97c5347bf3b13629ad0ffd10 | https://github.com/kagawa123/Person_reID_baseline_pytorch/tree/a503af2fa329406e97c5347bf3b13629ad0ffd10 |
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.... | caojiangxia/BiGI | Attention | false | 15,003 | [
"MIT"
] | 57 | ed54c20523a5b3f295b90a9c08f7c54e8258d04a | https://github.com/caojiangxia/BiGI/tree/ed54c20523a5b3f295b90a9c08f7c54e8258d04a |
RelevanceVector | import torch
import torch.nn as nn
class RelevanceVector(nn.Module):
def __init__(self, z_dim):
super(RelevanceVector, self).__init__()
self.rvlogit = nn.Parameter(0.001 * torch.randn(z_dim))
def forward(self):
rv = torch.sigmoid(self.rvlogit)
return self.rvlogit, rv
def ge... | 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... | seqam-lab/rfvae | RelevanceVector | false | 4,289 | [
"MIT"
] | 0 | 07089e2cca6d51f305731750c2c67b83a42df12a | https://github.com/seqam-lab/rfvae/tree/07089e2cca6d51f305731750c2c67b83a42df12a |
MLPPolicyNetwork | # 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
from to... | SAMMiCA/DL_based_E2E_Driving | MLPPolicyNetwork | false | 17,892 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
Discriminator | # 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_... | Phutoast/Win-or-Learn-Fast | Discriminator | false | 17,807 | [
"MIT"
] | 7 | 5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 | https://github.com/Phutoast/Win-or-Learn-Fast/tree/5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 |
EncoderLayer | # 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.... | iamxpy/pointer_summarizer | EncoderLayer | false | 6,878 | [
"Apache-2.0"
] | 1 | ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 | https://github.com/iamxpy/pointer_summarizer/tree/ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Conv2d(1, 1, 3)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 1, 64, 64])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | MattToast/SmartRedis | Net | false | 2,643 | [
"BSD-2-Clause"
] | 0 | de169429f05d1777ab68ae9fe20bfd34b51e9b81 | https://github.com/MattToast/SmartRedis/tree/de169429f05d1777ab68ae9fe20bfd34b51e9b81 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_dim, out_dim):
super(Model, self).__init__()
self.out_dim = out_dim
self.fc1 = nn.Linear(in_dim, in_dim // 2)
self.fc2 = nn.Linear(in_dim // 2, in_dim // 4)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MiscellaneousStuff/tlol-py | Model | false | 17,723 | [
"MIT"
] | 4 | 60477b4f794daa12930d7bbec4cf692bab426a33 | https://github.com/MiscellaneousStuff/tlol-py/tree/60477b4f794daa12930d7bbec4cf692bab426a33 |
SequenceBias | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Parameter
class SequenceBias(nn.Module):
"""
Adds one bias element to the end of the sequence.
so if the input has a shape ``(L, N, E)``, where
``L`` i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Pa... | EXAPPAI/opacus | SequenceBias | false | 2,178 | [
"Apache-2.0"
] | 0 | 11e188a2f03a8a08be51fdf2367cc1387879312a | https://github.com/EXAPPAI/opacus/tree/11e188a2f03a8a08be51fdf2367cc1387879312a |
LanguageModelCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Zhendong-Wang/arsm_image_captioning | LanguageModelCriterion | false | 9,661 | [
"MIT"
] | 0 | 2282b76ab03b53952269d94d6c4b19ab98636ca5 | https://github.com/Zhendong-Wang/arsm_image_captioning/tree/2282b76ab03b53952269d94d6c4b19ab98636ca5 |
Block | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob +... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | au55555/classification-pytorch | Block | false | 6,354 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
Enrichment | # 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_... | xavysp/TIN_xsp | Enrichment | false | 4,582 | [
"MIT"
] | 0 | 9f68e03923f637f4d4ef885694dfc3aaaaad6cea | https://github.com/xavysp/TIN_xsp/tree/9f68e03923f637f4d4ef885694dfc3aaaaad6cea |
get_normal | # 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.utils.data
import torch
import torch.nn as nn
import torch.sparse
a... | PrendiProgramming/UprightNet | get_normal | false | 2,727 | [
"MIT"
] | 0 | 73a0677079e27a806b48bf9ede70b8377002b2f3 | https://github.com/PrendiProgramming/UprightNet/tree/73a0677079e27a806b48bf9ede70b8377002b2f3 |
MMTM | # 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_... | haamoon/mmtm | MMTM | false | 15,486 | [
"MIT"
] | 70 | 1c81cfefad5532cfb39193b8af3840ac3346e897 | https://github.com/haamoon/mmtm/tree/1c81cfefad5532cfb39193b8af3840ac3346e897 |
FeatureCorrelation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | kensakurada/SceneChangeDet | FeatureCorrelation | false | 15,797 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HarryPham0123/FPT_data_centric_competition | MetaAconC | false | 5,290 | [
"Apache-2.0"
] | 1 | 3fa1e0ac48fdae2649b639229d9a74f75e461878 | https://github.com/HarryPham0123/FPT_data_centric_competition/tree/3fa1e0ac48fdae2649b639229d9a74f75e461878 |
LogSoftmax | import torch
import torch.nn.functional as F
class LogSoftmax(torch.nn.Module):
def __init__(self, dim):
super(LogSoftmax, self).__init__()
self.dim = dim
def forward(self, x, a):
nll = -F.log_softmax(x, self.dim, _stacklevel=5)
return (nll * a / a.sum(1, keepdim=True).clamp(... | 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
assert_size_stride = t... | Tiamat-Tech/just-ask | LogSoftmax | false | 14,520 | [
"Apache-2.0"
] | 59 | 80725161e12ad0682b4c2091f61a5889a335ba21 | https://github.com/Tiamat-Tech/just-ask/tree/80725161e12ad0682b4c2091f61a5889a335ba21 |
GateLayer | # 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
from torch.optim.lr_scheduler import *
assert_size_stride ... | xycforgithub/MultiTask-MRC | GateLayer | false | 16,753 | [
"BSD-3-Clause"
] | 105 | 6e5fe8b3cbc40058784cecad73219390e3c2a922 | https://github.com/xycforgithub/MultiTask-MRC/tree/6e5fe8b3cbc40058784cecad73219390e3c2a922 |
WNConv2d | import torch
import torch.nn as nn
import torch.utils.data
class WNConv2d(nn.Module):
"""Weight-normalized 2d convolution.
Args:
in_channels (int): Number of channels in the input.
out_channels (int): Number of channels in the output.
kernel_size (int): Side length of each convolutional kernel.
padding (... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Catherine0505/mar-scf-flow | WNConv2d | false | 17,093 | [
"Apache-2.0"
] | 10 | aa7c3564cb9f2967c5e580a633516dba1b597f98 | https://github.com/Catherine0505/mar-scf-flow/tree/aa7c3564cb9f2967c5e580a633516dba1b597f98 |
ResBlockWithFusedBN | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
class ResBlockWithFusedBN(nn.Module):
""" Bottleneck Residual Block """
def __init__(self, inplanes, outplanes, innerplanes, stride=1, dilation
=1, group=1, stride_1x1=True):
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 import triton_helpers
from torch import nn
from tor... | vadimadr/openvino_training_extensions | ResBlockWithFusedBN | false | 11,042 | [
"Apache-2.0"
] | 0 | 5d64b8423c8eb7b374ed629fad938359d34a07d2 | https://github.com/vadimadr/openvino_training_extensions/tree/5d64b8423c8eb7b374ed629fad938359d34a07d2 |
TestUpsampleNearest2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class TestUpsampleNearest2d(nn.Module):
"""Module for UpsampleNearest2d conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestUpsampleNearest2d, self).__init__()
self.conv2d = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | gqgs/pytorch2keras | TestUpsampleNearest2d | false | 15,476 | [
"MIT"
] | 733 | 9cd26e9e6698e1f07e455dbb94c15ecff53fb788 | https://github.com/gqgs/pytorch2keras/tree/9cd26e9e6698e1f07e455dbb94c15ecff53fb788 |
CosineDistance | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ParikhKadam/flair | CosineDistance | false | 14,154 | [
"MIT"
] | 7,539 | a1732bc5ab0b4aeb09d1ed3a630ae2fd8fa095ef | https://github.com/ParikhKadam/flair/tree/a1732bc5ab0b4aeb09d1ed3a630ae2fd8fa095ef |
Upsample | import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample(nn.Module):
def __init__(self, scale_factor=1, mode='nearest'):
super(Upsample, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
def forward(self, x):
return F.interpolate(x, 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... | CV-YYDS/YOLOv3 | Upsample | false | 11,274 | [
"MIT"
] | 0 | a433064721dfc932509aaed6cb44a785b24bc768 | https://github.com/CV-YYDS/YOLOv3/tree/a433064721dfc932509aaed6cb44a785b24bc768 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | hlesmqh/WS3D | SigmoidFocalClassificationLoss | false | 15,528 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
MLP | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Conv1D(nn.Module):
def __init__(self, nf, nx):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ExamDay/NeuralGREWT | MLP | false | 6,474 | [
"MIT"
] | 1 | 2256eb8c88f410bf5a229911f299b216153c96ba | https://github.com/ExamDay/NeuralGREWT/tree/2256eb8c88f410bf5a229911f299b216153c96ba |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.nn as nn
import tor... | AsianZeus/Diverse-Facial-Edit | ToRGB | false | 9,428 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class XavierLinear(nn.Module):
def __init__(self, d_in, d_out, bias=True):
super().__init__()
self.linear = nn.Linear(d_in, d_out, bias=bias)
nn.init.xavier_normal_(self.linear.weight)
def forward(self, x):
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
from torch._inductor.runtime.... | muberraozmen/MrMP | MultiHeadAttention | false | 4,052 | [
"MIT"
] | 0 | da6bcccbad85a682c848ff4aa1121c773d779e57 | https://github.com/muberraozmen/MrMP/tree/da6bcccbad85a682c848ff4aa1121c773d779e57 |
SEModule | import torch
import torch.nn as nn
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
self.relu = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ChrisLee63/reid-strong-baseline | SEModule | false | 11,301 | [
"MIT"
] | 0 | da755d3812da3c2e6e69920066badaad42f6fa6b | https://github.com/ChrisLee63/reid-strong-baseline/tree/da755d3812da3c2e6e69920066badaad42f6fa6b |
ImgPatches | import torch
import torch.nn as nn
import torch.utils.data
class ImgPatches(nn.Module):
def __init__(self, input_channel=3, dim=768, patch_size=4):
super().__init__()
self.patch_embed = nn.Conv2d(input_channel, dim, kernel_size=
patch_size, stride=patch_size)
def forward(self, im... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zoosecretbase/TransGAN | ImgPatches | false | 13,184 | [
"MIT"
] | 0 | f2546aec5b80bdddb2c8621a6e011532df3e2d73 | https://github.com/zoosecretbase/TransGAN/tree/f2546aec5b80bdddb2c8621a6e011532df3e2d73 |
ConcatBlock | import torch
import torch.nn as nn
import torch.nn.functional
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._... | timothytancy/SSL4MIS | ConcatBlock | false | 13,034 | [
"MIT"
] | 0 | 7879ad3483223e31a2785f5112eac1d4fa36b66e | https://github.com/timothytancy/SSL4MIS/tree/7879ad3483223e31a2785f5112eac1d4fa36b66e |
RandomShiftsAug | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language 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._d... | rajeswar18/url_benchmark | RandomShiftsAug | false | 16,313 | [
"MIT"
] | 180 | 2fdfd82a9067222106ef7627f71b1e1ae5d70a85 | https://github.com/rajeswar18/url_benchmark/tree/2fdfd82a9067222106ef7627f71b1e1ae5d70a85 |
PatchEmbed | # 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.parallel
assert_size_stride = torch._C._dy... | Inch-Z/volo | PatchEmbed | false | 11,516 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def scaled_dot_product_attention(query, keys, values, mask=None):
d_k = keys.shape[-1]
dot_score = query @ keys.transpose(-2, -1) / math.sqrt(d_k)
if mask is not None:
dot_score = dot_score.masked_fill(mask == 0, -10000... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NathanYanJing/TransformerReplication | DecoderLayer | false | 11,788 | [
"MIT"
] | 0 | b20f987dcc507724971f843c2d214c9c76bd8e34 | https://github.com/NathanYanJing/TransformerReplication/tree/b20f987dcc507724971f843c2d214c9c76bd8e34 |
ConformerFeedForward | # 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
import torch.utils.data
import torch.optim
assert_size_stri... | Oktai15/NeMo | ConformerFeedForward | false | 5,673 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
TVLoss | import torch
from torch import nn
import torch.utils.data
from torchvision.transforms import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_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 import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HamsterBiz/iSeeBetter | TVLoss | false | 11,633 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
AdaIN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GiangHLe/pytorch_GAN_zoo | AdaIN | false | 11,477 | [
"BSD-3-Clause"
] | 0 | 7a3db2a88032f357b3f262abd6204b560caa9f2c | https://github.com/GiangHLe/pytorch_GAN_zoo/tree/7a3db2a88032f357b3f262abd6204b560caa9f2c |
AttentionModule | import torch
from torch import nn
import torch.nn.functional as F
class AttentionModule(nn.Module):
def __init__(self, d_model, d_k=None, device='cpu', dropout=None):
super().__init__()
if not d_k:
d_k = d_model
self.W = nn.Parameter(torch.randn(d_model, d_model, device=device... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BruceWen120/medical-abbreviation-pretraining | AttentionModule | false | 13,444 | [
"Apache-2.0",
"MIT"
] | 125 | 333e49461f7463e97515f949f441c7ac8af7d980 | https://github.com/BruceWen120/medical-abbreviation-pretraining/tree/333e49461f7463e97515f949f441c7ac8af7d980 |
OutConv | # 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... | AyoubOuddah/SAR_River_Segmentation_Pytorch-Unet | OutConv | false | 16,958 | [
"MIT"
] | 10 | e7da2414d6158a5f6358df92ded273a1a016cb91 | https://github.com/AyoubOuddah/SAR_River_Segmentation_Pytorch-Unet/tree/e7da2414d6158a5f6358df92ded273a1a016cb91 |
BiTemperedLogisticLoss | # 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... | GenoM87/cassava_leaf | BiTemperedLogisticLoss | false | 2,283 | [
"MIT"
] | 0 | 51cc78b99a687b2d38be1930c40fc8aef3105b42 | https://github.com/GenoM87/cassava_leaf/tree/51cc78b99a687b2d38be1930c40fc8aef3105b42 |
Contraster | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class Contraster(AdversarialNoiseGenerator):
def __init__(self, eps... | 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.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | Contraster | false | 7,685 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
OrthogonalHouseholder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | mahkons/orthogonal | OrthogonalHouseholder | false | 3,959 | [
"MIT"
] | 0 | 19a69134ca9a01ef564eab624b8c1526291770aa | https://github.com/mahkons/orthogonal/tree/19a69134ca9a01ef564eab624b8c1526291770aa |
WeightedSoftDiceLoss | # 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | jayden-chua/image-mask | WeightedSoftDiceLoss | false | 3,694 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
Beta2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | samarth-robo/apex | Beta2 | false | 4,256 | [
"MIT"
] | 0 | db24044acacd0fcd006886eb1677eaa2f2beedad | https://github.com/samarth-robo/apex/tree/db24044acacd0fcd006886eb1677eaa2f2beedad |
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... | AdamLohSg/GTA | TemporalEmbedding | false | 16,892 | [
"Apache-2.0"
] | 8 | bf6a745a6e28e365466e76360a15ca10ce61e009 | https://github.com/AdamLohSg/GTA/tree/bf6a745a6e28e365466e76360a15ca10ce61e009 |
Value | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Value(nn.Module):
def __init__(self, state_dim, action_dim):
super(Value, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 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
import ... | Thibaud-Ardoin/d4rl_evaluations | Value | false | 14,483 | [
"Apache-2.0"
] | 123 | 135b23d3aecc234aacaeaaa019fbc7101d9b87ec | https://github.com/Thibaud-Ardoin/d4rl_evaluations/tree/135b23d3aecc234aacaeaaa019fbc7101d9b87ec |
MPJPELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ZephyrII/mmpose_charger | MPJPELoss | false | 12,029 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
L2Norm | import torch
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Guido27/project_vg | L2Norm | false | 9,109 | [
"MIT"
] | 0 | 3322fc355742929f43f3d97204398035645d968c | https://github.com/Guido27/project_vg/tree/3322fc355742929f43f3d97204398035645d968c |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | gist-ailab/uoais | NaiveGroupNorm | false | 15,448 | [
"BSD-2-Clause"
] | 52 | fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 | https://github.com/gist-ailab/uoais/tree/fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 |
ContrastiveLoss | # 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 torch.nn.modules
import torch.nn
import torch.nn.init
import... | JoshuaBeard/netharn | ContrastiveLoss | false | 676 | [
"Apache-2.0"
] | 0 | 90773542c47363e663ee58f20fd151eb89bc313b | https://github.com/JoshuaBeard/netharn/tree/90773542c47363e663ee58f20fd151eb89bc313b |
CriticNetwork | # 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... | yutiansut/Personae | CriticNetwork | false | 16,778 | [
"MIT"
] | 1,046 | e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | https://github.com/yutiansut/Personae/tree/e5e89cbaaf2c4708952d25fdb25e99837aecdb4e |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
... | monimoyd/project_deep_reinforcement_learning_collaboration_competition | Actor | false | 12,793 | [
"MIT"
] | 0 | 3782abb839b671ea53ece1435a4d481d7871cd39 | https://github.com/monimoyd/project_deep_reinforcement_learning_collaboration_competition/tree/3782abb839b671ea53ece1435a4d481d7871cd39 |
FakeRKHSConvNet | # 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.... | oke-aditya/pytorch-lightning-bolts | FakeRKHSConvNet | false | 7,362 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
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