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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
from torch.nn import init as... | RunqiuBao/Event_ESTRNN | L1_Charbonnier_loss | false | 14,329 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
SiQU | import torch
class SiQU(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | chris-price19/ocp | SiQU | false | 1,700 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | almiliMSFT/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 14,815 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
DotProductAttention | # 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.... | tompoek/Listen-Attend-Spell-v2 | DotProductAttention | false | 10,856 | [
"MIT"
] | 0 | aa19543c9d23256a007d6e7a98d9cbc571e89f7f | https://github.com/tompoek/Listen-Attend-Spell-v2/tree/aa19543c9d23256a007d6e7a98d9cbc571e89f7f |
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.... | Mrpatekful/supervised-translation | Attention | false | 5,627 | [
"MIT"
] | 1 | d03db6a0fc25900fd42b8057a12adad0b8d025f8 | https://github.com/Mrpatekful/supervised-translation/tree/d03db6a0fc25900fd42b8057a12adad0b8d025f8 |
DurationPredictorLoss | import torch
class DurationPredictorLoss(torch.nn.Module):
"""Loss function module for duration predictor.
The loss value is Calculated in log domain to make it Gaussian.
Args:
offset (float, optional): Offset value to avoid nan in log domain.
"""
def __init__(self, offset=1.0):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | akreal/end-to-end-slu-espnet | DurationPredictorLoss | false | 3,086 | [
"Apache-2.0"
] | 0 | 0b16dc8b10b31a4567b3312678a753a94bb200da | https://github.com/akreal/end-to-end-slu-espnet/tree/0b16dc8b10b31a4567b3312678a753a94bb200da |
BiLinearSim | # 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.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | johnson7788/mt-dnn | BiLinearSim | false | 3,896 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
AddPositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | AddPositionalEncoding | false | 3,770 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
InternalQNetwork | # 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_... | Josh-Joseph/tsc-2019 | InternalQNetwork | false | 2,428 | [
"MIT"
] | 0 | 0cb68b69448257ec7fd8d9edaf6b8aa165599554 | https://github.com/Josh-Joseph/tsc-2019/tree/0cb68b69448257ec7fd8d9edaf6b8aa165599554 |
ComplexConvTranspose2d | import torch
import torch.nn as nn
class ComplexConvTranspose2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, dilation=1, groups=1, bias=True, **kwargs
):
super().__init__()
self.tconv_re = nn.ConvTranspose2d(in_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | jonashaag/PhoneFortifiedPerceptualLoss | ComplexConvTranspose2d | false | 3,769 | [
"MIT"
] | 0 | 1dabdd4203f59c2d1bfe22bffc4c63b204aa50bd | https://github.com/jonashaag/PhoneFortifiedPerceptualLoss/tree/1dabdd4203f59c2d1bfe22bffc4c63b204aa50bd |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Net(nn.Module):
def __init__(self, s_dim, a_dim):
super(Net, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | SeungyounShin/pytorch-A3C | Net | false | 1,040 | [
"MIT"
] | 0 | acb9c05a5e1a697c48a7d4c1a48b1c86326faf91 | https://github.com/SeungyounShin/pytorch-A3C/tree/acb9c05a5e1a697c48a7d4c1a48b1c86326faf91 |
LSTM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | lwaekfjlk/Light-the-Torch | LSTM | false | 7,140 | [
"MIT"
] | 1 | eed1df3d28016aee86385959b5e94e2108ee0571 | https://github.com/lwaekfjlk/Light-the-Torch/tree/eed1df3d28016aee86385959b5e94e2108ee0571 |
DAInsHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | thesuperorange/Domain-Adaptive-Faster-RCNN-PyTorch | DAInsHead | false | 13,072 | [
"MIT"
] | 0 | bcde744a486b25ec1d6e4b023da3ce0c8e5d72a7 | https://github.com/thesuperorange/Domain-Adaptive-Faster-RCNN-PyTorch/tree/bcde744a486b25ec1d6e4b023da3ce0c8e5d72a7 |
Encoder | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create a lis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | joowlim/pytorch-3dunet | Encoder | false | 10,405 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 |
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_... | FacePerceiver/FaRL | LayerNorm | false | 8,098 | [
"MIT"
] | 23 | 38f1d32f4e63940fae524e9f501b88a947ec09cd | https://github.com/FacePerceiver/FaRL/tree/38f1d32f4e63940fae524e9f501b88a947ec09cd |
Logits | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class Logits(nn.Module):
"""
Do Deep Nets Really Need to be Deep?
http://papers.nips.cc/paper/5484-do-deep-nets-really-need-to-be-deep.pdf
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._utils
from itertools import product as product
import... | Capetian/FaceX-Zoo | Logits | false | 4,963 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | rharish101/CIL-Project | ContrastiveLoss | false | 4,190 | [
"MIT"
] | 0 | fed1be8b22bb4228329b719a301f74459a7bf13b | https://github.com/rharish101/CIL-Project/tree/fed1be8b22bb4228329b719a301f74459a7bf13b |
MultiheadSimilarity | # 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
from torch import nn
assert_size_stride = torch._C._dyna... | bladewaltz1/clipvid-tmp | MultiheadSimilarity | false | 1,564 | [
"MIT"
] | 0 | 8a4a990c318fdfbf6dac443abd3bc16637abba3d | https://github.com/bladewaltz1/clipvid-tmp/tree/8a4a990c318fdfbf6dac443abd3bc16637abba3d |
AdapterLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DAQuestionAnswering/Bert-n-Pals | AdapterLayer | false | 7,620 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
GCN_encoder | import torch
import torch.nn as nn
import torch.nn.init as init
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim):
super(GraphConv, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.weight = nn.Parameter(torch.FloatTensor(input_dim, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | jonathangomesselman/graph-generation | GCN_encoder | false | 6,980 | [
"MIT"
] | 1 | 72a8be30d54a414fcca9ea0fad1a62e38b85ee2f | https://github.com/jonathangomesselman/graph-generation/tree/72a8be30d54a414fcca9ea0fad1a62e38b85ee2f |
SpectrogramMasker | # 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.triton_helpers import libdevice
import torch.nn as ... | AppleHolic/2020AIChallengeSpeechRecognition | SpectrogramMasker | false | 16,941 | [
"MIT"
] | 9 | 62002f036a4bb4ab23f7bdba73f19e97e0ac7087 | https://github.com/AppleHolic/2020AIChallengeSpeechRecognition/tree/62002f036a4bb4ab23f7bdba73f19e97e0ac7087 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal(layer.weight, mean=0.0, std=0.3)
nn.init.constant(layer.bias, 0.3)
class Net(nn.Module):
def __init__(self, s_dim, a_dim):
super(Net, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | HaiyinPiao/pytorch-a3c | Net | false | 9,060 | [
"MIT"
] | 0 | d151fb4197449610f090c1d687c50a74422f594c | https://github.com/HaiyinPiao/pytorch-a3c/tree/d151fb4197449610f090c1d687c50a74422f594c |
NoisyLinear | import math
import torch
import torch.nn as nn
import torch.nn
import torch.optim
class NoisyLinear(nn.Linear):
def __init__(self, in_dimension, out_dimension, std_dev_init=0.4) ->None:
"""
Noisy Networks for Exploration: https://arxiv.org/abs/1706.10295
Standard linear layer: y = wx + b
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libd... | johncliu/Horizon | NoisyLinear | false | 3,766 | [
"BSD-3-Clause"
] | 0 | cfa7a873ada5de3bb01e78e2f237d9849b8270b2 | https://github.com/johncliu/Horizon/tree/cfa7a873ada5de3bb01e78e2f237d9849b8270b2 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16 * 28 * 28, 512)
self.fc2 = nn.Linear(512, 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dollarkillerx/PyTorchStudy | Net | false | 10,022 | [
"MIT"
] | 0 | c17b2973c89e3a2f088513f29bd5eb6f47957585 | https://github.com/dollarkillerx/PyTorchStudy/tree/c17b2973c89e3a2f088513f29bd5eb6f47957585 |
ResidualBlock_noBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | hduba/KAIR | ResidualBlock_noBN | false | 3,613 | [
"MIT"
] | 0 | dbd7596c7e4a4667b9b7baac369fc6c02571fa58 | https://github.com/hduba/KAIR/tree/dbd7596c7e4a4667b9b7baac369fc6c02571fa58 |
SharedAgent | import torch
import torch.nn.functional as F
import torch.nn as nn
class SharedAgent(torch.nn.Module):
"""
A simple two headed / chimera Actor Critic agent.
The actor and critic share the body of the network.
It is argued that this is because "good" actions
correlate to visiting states with "larg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mpgussert/fundamentalRL | SharedAgent | false | 7,280 | [
"MIT"
] | 1 | 4f45436226e0823c21cac316dec8bbf1df697467 | https://github.com/mpgussert/fundamentalRL/tree/4f45436226e0823c21cac316dec8bbf1df697467 |
PositionGenerator | # 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 ... | odb9402/MAT | PositionGenerator | false | 4,111 | [
"MIT"
] | 0 | 95d8083170da2c8ce1f5898b3a556bcf54eac8cc | https://github.com/odb9402/MAT/tree/95d8083170da2c8ce1f5898b3a556bcf54eac8cc |
KopoinANNNetwork | # 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... | bmd2007/benchmark_eval | KopoinANNNetwork | false | 6,344 | [
"MIT"
] | 1 | aa42bb3369e79db4cb63e1963afcc8af6d8f5696 | https://github.com/bmd2007/benchmark_eval/tree/aa42bb3369e79db4cb63e1963afcc8af6d8f5696 |
GeneralizedMeanPooling | import torch
import torch.nn as nn
class GeneralizedMeanPooling(nn.Module):
"""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 gets Max Pooling
- At p = 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | tenghehan/reid_without_id | GeneralizedMeanPooling | false | 10,868 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 |
LinearMaxPoolLinearModel | import torch
import torch.nn as nn
class LinearMaxPoolLinearModel(nn.Module):
def __init__(self):
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bias=False... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Europium248/captum | LinearMaxPoolLinearModel | false | 436 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
MatchModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> impo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ambitioner-c/MatchZoo-py | MatchModule | false | 13,275 | [
"Apache-2.0"
] | 468 | bb088edce8e01c2c2326ca1a8ac647f0d23f088d | https://github.com/Ambitioner-c/MatchZoo-py/tree/bb088edce8e01c2c2326ca1a8ac647f0d23f088d |
BasicNN | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class BasicNN(nn.Module):
def __init__(self):
super(BasicNN, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, x):
if isinstance(x, np.ndarray):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dbczumar/clipper | BasicNN | false | 3,401 | [
"Apache-2.0"
] | 0 | 80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d | https://github.com/dbczumar/clipper/tree/80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d |
CNN | # 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 ... | NanoGDA/gda-extraction | CNN | false | 17,738 | [
"MIT"
] | 4 | 9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 | https://github.com/NanoGDA/gda-extraction/tree/9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 |
AdaptiveSquare | # 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.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | ndem0/PINA | AdaptiveSquare | false | 10,719 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class Norm(nn.Module):
def __init__(self, emb_dim, eps=1e-06):
super().__init__()
self.size = emb_dim
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.ze... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | chandar-lab/CriticalGradientOptimization | DecoderLayer | false | 6,451 | [
"MIT"
] | 1 | 1af4b1df40489991289bb50bb69859a00b2c97c6 | https://github.com/chandar-lab/CriticalGradientOptimization/tree/1af4b1df40489991289bb50bb69859a00b2c97c6 |
L2 | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | qwopqwop200/Fast-Invertible-Rescaling-Net | L2 | false | 7,524 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
PointerAttention | # 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.... | jamaalhay/Final_Proj | PointerAttention | false | 15,665 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
Normalize | import torch
from torch import nn
class Normalize(nn.Module):
"""normalization layer"""
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out = x.div(... | 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... | JJuOn/Few-shot_Class_Incremental_Learning | Normalize | false | 5,362 | [
"MIT"
] | 1 | a2178051a6fefcd73b60f5e4236116bf828a801c | https://github.com/JJuOn/Few-shot_Class_Incremental_Learning/tree/a2178051a6fefcd73b60f5e4236116bf828a801c |
PolarityInversion | import torch
class PolarityInversion(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, audio):
audio = torch.neg(audio)
return audio
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | h0ngwen/torchaudio-augmentations | PolarityInversion | false | 6,774 | [
"MIT"
] | 1 | d044f9d020e12032ab9280acf5f34a337e72d212 | https://github.com/h0ngwen/torchaudio-augmentations/tree/d044f9d020e12032ab9280acf5f34a337e72d212 |
SoftGate | import torch
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
class SoftGate(nn.Module):
COEFF = 12.0
def forward(self, x):
return torch.sigmoid(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 as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.ut... | hyunobae/BasicSR | SoftGate | false | 12,519 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
MaxMinGroup | import torch
import torch.nn as nn
def process_maxmin_groupsize(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
format(num_channels, num_un... | 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... | lingzenan/invertible-resnet | MaxMinGroup | false | 7,094 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
AconC | # 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... | nmaac/acon | AconC | false | 16,178 | [
"MIT"
] | 163 | 99fd67928a6ffb0543b54614303caada96c756f5 | https://github.com/nmaac/acon/tree/99fd67928a6ffb0543b54614303caada96c756f5 |
KL_loss_softmax | import torch
import torch.nn as nn
import torch.nn.init
class KL_loss_softmax(nn.Module):
"""
Compute KL_divergence between all prediction score (already sum=1, omit softmax function)
"""
def __init__(self):
super(KL_loss_softmax, self).__init__()
self.KL_loss = nn.KLDivLoss(reduce=Fa... | 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... | AndresPMD/semantic_adaptive_margin | KL_loss_softmax | false | 7,652 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertOutput(nn.Module):
def __init__(self, config):
super(BertOutput, self).__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_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.triton_helpers import libdevice
import torch.nn as ... | Worm4047/TVR | BertOutput | false | 14,590 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
"""利用卷积 + 最大池化得到句子嵌入"""
def __init__(self, max_length, word_embedding_dim=50, pos_embedding_dim
=5, hidden_size=230):
nn.Module.__init__(self)
self.max_length = max_length
self.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 import triton_helpers
import torch.nn as nn
import ... | Hao-Kailong/DisFeb | Encoder | false | 522 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
ZeroPad1d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | 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.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | AbhilashMathews/adahessian | ZeroPad1d | false | 4,833 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 |
L2Norm | import torch
from torch import nn
from torchvision.models.resnet import *
class L2Norm(nn.Module):
def forward(self, x, eps=1e-06):
norm = x.norm(dim=1, keepdim=True).clamp(min=eps)
return x / norm
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | DSciLab/SSLab | L2Norm | false | 826 | [
"MIT"
] | 0 | 9eeef8cebfa01b079779259a2ded4138bf54c1ff | https://github.com/DSciLab/SSLab/tree/9eeef8cebfa01b079779259a2ded4138bf54c1ff |
MaskedTemporalPooling | import torch
from typing import Optional
import torch.utils.data
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
... | 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... | TheShadow29/pytorchvideo | MaskedTemporalPooling | false | 9,697 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
PixelNorm | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
class PixelNorm(nn.Module):
def __init__(self, epsilon=1e-08):
"""
@notice: avoid in-place ops.
https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-com... | 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
import torch.nn.parallel
import torch.utils.data
import to... | IdanAzuri/MixMatch-pytorch | PixelNorm | false | 579 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
PTLogreg | # 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.... | EduardEdiJerkovic/deeplearning | PTLogreg | false | 8,995 | [
"MIT"
] | 0 | 0493b26ca153f93f41e8de930e16df658fb01a56 | https://github.com/EduardEdiJerkovic/deeplearning/tree/0493b26ca153f93f41e8de930e16df658fb01a56 |
BiDAFAttention | # 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.... | amankhullar/MMBiDAF | BiDAFAttention | false | 18,294 | [
"MIT"
] | 4 | 510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 | https://github.com/amankhullar/MMBiDAF/tree/510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 |
SiameseCNN | import torch
from torch import nn
from torch.nn import functional as F
class SiameseCNN(nn.Module):
"""
basic structure similar to the CNN
input is splited into two 1*14*14 images for separating training, share the same parameters
"""
def __init__(self):
super(SiameseCNN, self).__init__()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | EE559DeepLearningEPFL/Project1 | SiameseCNN | false | 408 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ShadowTwin41/alpha-WGAN-SigmaRat | Encoder | false | 11,885 | [
"MIT"
] | 0 | 051bb8c5d7b8248e9c724d3de87c0fd771d7070f | https://github.com/ShadowTwin41/alpha-WGAN-SigmaRat/tree/051bb8c5d7b8248e9c724d3de87c0fd771d7070f |
NsSymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | 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.functi... | kiminh/mt-dnn | NsSymKlCriterion | false | 7,034 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
SAM_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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | XiuhengWang/Sylvester_TSFN_MDC_HSI_superresolution | SAM_Loss | false | 18,098 | [
"MIT"
] | 5 | f70799c931d44d5d6cac635ef539a38bc573c7d9 | https://github.com/XiuhengWang/Sylvester_TSFN_MDC_HSI_superresolution/tree/f70799c931d44d5d6cac635ef539a38bc573c7d9 |
PSA_p | import torch
import torch.nn as nn
import torch._utils
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def kaiming_init(module, a=0, mode='fan_out', nonlinearity='relu', bias=0,
distribution='normal'):
assert distribution in ['uniform', 'normal']
if ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | realphongha/human-pose-estimation.pytorch | PSA_p | false | 4,189 | [
"MIT"
] | 0 | 29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f | https://github.com/realphongha/human-pose-estimation.pytorch/tree/29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f |
MLPClassifier | # 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.... | UKPLab/curriculum-annotation | MLPClassifier | false | 9,555 | [
"Apache-2.0"
] | 0 | 1d6ca490ea180019bb09d1d3818874f4321d4d0f | https://github.com/UKPLab/curriculum-annotation/tree/1d6ca490ea180019bb09d1d3818874f4321d4d0f |
UpConv | # 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 collections import OrderedDict
import torch.nn as nn
assert_size_stride = t... | HCMUS-ROBOTICS/ssdf-perception | UpConv | false | 9,067 | [
"MIT"
] | 0 | c3eb426397a542da49509bb381972c8ff877597b | https://github.com/HCMUS-ROBOTICS/ssdf-perception/tree/c3eb426397a542da49509bb381972c8ff877597b |
Fp32GroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | AbhilashMathews/adahessian | Fp32GroupNorm | false | 4,836 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 |
EuclideanComparator_1 | import torch
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from dataclasses import data... | lavis-nlp/irtm | EuclideanComparator_1 | false | 10,407 | [
"MIT"
] | 0 | e6c96519918795cfaa0c09ef2d4164f451265518 | https://github.com/lavis-nlp/irtm/tree/e6c96519918795cfaa0c09ef2d4164f451265518 |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def forward(self, t, y):
return 2 * y / t + t ** 4 * torch.sin(2 * t) - t ** 2 + 4 * t ** 3
def y_exact(self, t):
return -0.5 * t ** 4 * torch.cos(2 * t) + 0.5 * t ** 3 * torch.sin(
2 * t) + 0.25 * t ** 2 * torch.cos(2 * t)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | gaozhihan/torchdiffeq | SineODE | false | 6,713 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
ATOCAttentionUnit | import torch
from typing import Union
import torch.nn as nn
from typing import Dict
import torch.utils.data
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Hcnaeg/DI-engine | ATOCAttentionUnit | false | 2,377 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BIT-ENGD/eeqa | BertAttention | false | 15,383 | [
"MIT"
] | 142 | 2995abbaff1fb47131246a247ee7ed62aa94f4c3 | https://github.com/BIT-ENGD/eeqa/tree/2995abbaff1fb47131246a247ee7ed62aa94f4c3 |
SpatialRescaler | # 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 functools import partial
import torch.nn as nn
assert_size_stride = torch._C._dynamo... | transat/latent-diffusion | SpatialRescaler | false | 10,923 | [
"MIT"
] | 0 | 1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 | https://github.com/transat/latent-diffusion/tree/1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim, ... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | DeterjoSimon/dtu_mlops | Encoder | false | 11,346 | [
"Apache-2.0"
] | 0 | 6484be509c002690b995f399001704c6b0bb42e4 | https://github.com/DeterjoSimon/dtu_mlops/tree/6484be509c002690b995f399001704c6b0bb42e4 |
sum_squared_error | import torch
from torch.nn.modules.loss import _Loss
class sum_squared_error(_Loss):
"""
Definition: sum_squared_error = 1/2 * nn.MSELoss(reduction = 'sum')
The backward is defined as: input-target
"""
def __init__(self, size_average=None, reduce=None, reduction='sum'):
super(sum_squared_... | 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.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | ORNL/AADL | sum_squared_error | false | 17,760 | [
"BSD-3-Clause"
] | 6 | 8a509676d0a0a78f1f334a3dc93e92721cfcfe90 | https://github.com/ORNL/AADL/tree/8a509676d0a0a78f1f334a3dc93e92721cfcfe90 |
ConstractiveThresholdHingeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConstractiveThresholdHingeLoss(nn.Module):
def __init__(self, hingethresh=0.0, margin=2.0):
super(ConstractiveThresholdHingeLoss, self).__init__()
self.threshold = hingethresh
self.margin = margin
def forward(se... | 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... | kensakurada/SceneChangeDet | ConstractiveThresholdHingeLoss | false | 15,808 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
FreqUpsample | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class FreqUpsample(nn.Module):
def __init__(self, factor: 'int', mode='nearest'):
super().__init__()
self.f = float(factor)
self.mode = mode
def forward(self, x: 'Tensor') ->Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | JinmingChe/DeepFilterNet | FreqUpsample | false | 5,396 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 1 | 0e35a24c33c091b4c34afb3599f2945bf5e87adf | https://github.com/JinmingChe/DeepFilterNet/tree/0e35a24c33c091b4c34afb3599f2945bf5e87adf |
Softmax_T | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Softmax_T(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super(Softmax_T, self).__init__()
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LakeAndCat/CluOReg | Softmax_T | false | 748 | [
"MIT"
] | 0 | ba50cb056061b3833050d32e532e08152bdc8de2 | https://github.com/LakeAndCat/CluOReg/tree/ba50cb056061b3833050d32e532e08152bdc8de2 |
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.... | dashstander/glide-text2im | ResidualAttentionBlock | false | 1,814 | [
"MIT"
] | 0 | 58f03a871ee0567e27fccc40df98203e675a9b8e | https://github.com/dashstander/glide-text2im/tree/58f03a871ee0567e27fccc40df98203e675a9b8e |
DownConv | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1, padding=1, bias=True, groups=1
):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=
stride, padding=padding, bias=bias, groups=groups)
class D... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Amadeus9029/Haru | DownConv | false | 9,071 | [
"MIT"
] | 0 | 60396b6cc7ad008e4ae78cb182b6f421197cd7bf | https://github.com/Amadeus9029/Haru/tree/60396b6cc7ad008e4ae78cb182b6f421197cd7bf |
MultiHeadedAttention | import torch
import numpy as np
import torch.utils.data
class ScaledDotProductAttention(torch.nn.Module):
"""
Scaled, softmax attention module for Transformer as defined by
Attention(Q, K, V) on pg 4. Returns the final attention vectors as well as
the attention matrices (pairwise scores). """
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.... | icemansina/protein-transformer | MultiHeadedAttention | false | 6,861 | [
"BSD-3-Clause"
] | 1 | 4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb | https://github.com/icemansina/protein-transformer/tree/4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb |
DocUnetLossPow | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | hologerry/DewarpNet | DocUnetLossPow | false | 3,609 | [
"MIT"
] | 0 | b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 | https://github.com/hologerry/DewarpNet/tree/b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 |
encoderDepth | # 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.... | Miles629/TransparentShapeRealData | encoderDepth | false | 14,095 | [
"MIT"
] | 91 | b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | https://github.com/Miles629/TransparentShapeRealData/tree/b81098a2d1882f5fd33fba6167d7258dbe02d6d2 |
EALSTM | # 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 ... | danielsuo/toy_flood | EALSTM | false | 15,128 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BHD233/PaddleOCR2Pytorch | MultiHeadAttention | false | 13,364 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
PNTrainingSigmoid | import torch
from torch import nn
class PNTrainingSigmoid(nn.Module):
def __init__(self):
super(PNTrainingSigmoid, self).__init__()
return
def forward(self, output_p, output_n, prior):
cost = prior * torch.mean(torch.sigmoid(-output_p))
cost = cost + (1 - prior) * torch.mean(... | 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... | mxuq/Imbalance-PU | PNTrainingSigmoid | false | 7,307 | [
"MIT"
] | 1 | fd4403b05f98ca6bc8156783e8275888d63f6435 | https://github.com/mxuq/Imbalance-PU/tree/fd4403b05f98ca6bc8156783e8275888d63f6435 |
LinearAdd | # 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.cuda
import torch.backends.cudnn
import torch.... | yangw1234/intel-extension-for-pytorch | LinearAdd | false | 10,958 | [
"Apache-2.0"
] | 0 | 571e31578605ab3999dcebbb4d66a0ee2253a464 | https://github.com/yangw1234/intel-extension-for-pytorch/tree/571e31578605ab3999dcebbb4d66a0ee2253a464 |
ASPP | import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, in_channel=256, depth=256):
super(ASPP, self).__init__()
self.mean = nn.AdaptiveAvgPool2d((1, 1))
self.conv = nn.Conv2d(in_channel, depth, 1, 1)
self.atrous_block1 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DoggyLiu0116/MamboNet | ASPP | false | 5,113 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
Decoder | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, in_channels, latent_size):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.in_channels = in_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | Adwaver4157/WorldModel_for_FinRL | Decoder | false | 4,794 | [
"MIT"
] | 1 | 0aa0a984aadffe0f6f2e83e55678c0e9304fba05 | https://github.com/Adwaver4157/WorldModel_for_FinRL/tree/0aa0a984aadffe0f6f2e83e55678c0e9304fba05 |
Network | import torch
class Network(torch.nn.Module):
def __init__(self, input_dimension, output_dimension):
super(Network, self).__init__()
self.layer_1 = torch.nn.Linear(in_features=input_dimension,
out_features=90)
self.layer_2 = torch.nn.Linear(in_features=90, out_features=125)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | joshsia/random-maze-rl | Network | false | 10,302 | [
"MIT"
] | 0 | 016b67d23bfba63182cf06ca17bc9a75baca6ee5 | https://github.com/joshsia/random-maze-rl/tree/016b67d23bfba63182cf06ca17bc9a75baca6ee5 |
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
import torch.optim
import torch.autograd
import torch.nn
... | FilippoC/-deep-syntactic-dependency-parsing-release | LayerNorm | false | 17,280 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
LearnedUpUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | hmdliu/PCGNet | LearnedUpUnit | false | 3,598 | [
"MIT"
] | 0 | c03f25dc1b138afc52f612c1c517b61874baa02a | https://github.com/hmdliu/PCGNet/tree/c03f25dc1b138afc52f612c1c517b61874baa02a |
LinearWithGroupNorm | import torch
import torch.utils.data
from torch import nn
from math import gcd
import torch.cuda
class LinearWithGroupNorm(nn.Module):
def __init__(self, n_in: 'int', n_out: 'int', num_groups: 'int'=32,
activation: 'bool'=True) ->None:
"""
Linear layer used in LaneGCN.
:param n_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.... | MCZhi/nuplan-devkit | LinearWithGroupNorm | false | 795 | [
"Apache-2.0"
] | 0 | 3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c | https://github.com/MCZhi/nuplan-devkit/tree/3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c |
FCLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | LostCow/KLUE | FCLayer | false | 8,480 | [
"MIT"
] | 18 | 73b1b0526cf6b1b6f5ef535b9527d8abe6ca1a77 | https://github.com/LostCow/KLUE/tree/73b1b0526cf6b1b6f5ef535b9527d8abe6ca1a77 |
SimulatorReward | # 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.... | karshtharyani/DeepReinforcementLearningInAction | SimulatorReward | false | 12,663 | [
"MIT"
] | 0 | 9dc40a43b43f05daf9aecb7e3ec7592cf38720e5 | https://github.com/karshtharyani/DeepReinforcementLearningInAction/tree/9dc40a43b43f05daf9aecb7e3ec7592cf38720e5 |
ZeroModule | import torch
import torch as th
from torch import nn
import torch.random
class ZeroModule(nn.Module):
"""Module that always returns zeros of same shape as input."""
def __init__(self, features_dim: 'int'):
"""Builds ZeroModule."""
super().__init__()
self.features_dim = features_dim
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.random
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | TaoHuang13/imitation | ZeroModule | false | 9,508 | [
"MIT"
] | 0 | f979be0fa05106754f6d1e5a98495d0fedbea598 | https://github.com/TaoHuang13/imitation/tree/f979be0fa05106754f6d1e5a98495d0fedbea598 |
GenNoise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | GuYuanjie/Deep-Retinex-fusion | GenNoise | false | 17,343 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
UpsampleConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Arjuna197/examples | UpsampleConvLayer | false | 11,372 | [
"BSD-3-Clause"
] | 0 | f504ea2aafc8a8baa5effb659fc1c20a70aabdda | https://github.com/Arjuna197/examples/tree/f504ea2aafc8a8baa5effb659fc1c20a70aabdda |
Reorg | # 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.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
assert_... | ChitienSun/NCTU_DLSR_final_project | Reorg | false | 252 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
SelfAttention2d | # 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.... | technillogue/v-diffusion-pytorch | SelfAttention2d | false | 4,418 | [
"MIT"
] | 0 | 3aa8c7f32adbde1d1ea3a9650004ffafabe5221b | https://github.com/technillogue/v-diffusion-pytorch/tree/3aa8c7f32adbde1d1ea3a9650004ffafabe5221b |
AffineChannelwise | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | dniku/dl-norms | AffineChannelwise | false | 6,585 | [
"MIT"
] | 1 | 0f1eef942bd318ac988ec7dfa9caea300d17e82a | https://github.com/dniku/dl-norms/tree/0f1eef942bd318ac988ec7dfa9caea300d17e82a |
DistillationLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | sithu31296/image_classification | DistillationLoss | false | 16,468 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
ConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | lawson-source/mtad-gat-pytorch | ConvLayer | false | 15,876 | [
"MIT"
] | 93 | 9e671ea99dedd82ac55f53e53af1d1b56c13ebff | https://github.com/lawson-source/mtad-gat-pytorch/tree/9e671ea99dedd82ac55f53e53af1d1b56c13ebff |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | BUPT-PRIV/BalancedGroupSoftmax | SmoothL1Loss | false | 13,374 | [
"Apache-2.0"
] | 333 | 90e04fd8ccecd2bc61bbe6053a741ae708da2794 | https://github.com/BUPT-PRIV/BalancedGroupSoftmax/tree/90e04fd8ccecd2bc61bbe6053a741ae708da2794 |
AuxsiameseMLP | # 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... | EE559DeepLearningEPFL/Project1 | AuxsiameseMLP | false | 388 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
Split | import torch
import torch.nn as nn
class Split(nn.Module):
def __init__(self):
super(Split, self).__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def inverse(self, x1, x2):
... | 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... | Schwartz-Zha/My-invertible-resnet | Split | false | 1,029 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
GenNoise | # 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.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DDQXZcp/FYP_ProjectFile_TANG_Zhiheng | GenNoise | false | 8,944 | [
"MIT"
] | 0 | b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d | https://github.com/DDQXZcp/FYP_ProjectFile_TANG_Zhiheng/tree/b0e3b9d1c5cee61e1d09a32e405244bda09b6f0d |
MaxPool | import torch
import torch.nn as nn
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride):
super(MaxPool, self).__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride)
def forward(self, x):
x = self.pool(x)
return x
def get_inputs():
retur... | 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... | Hiroaki-Ozaki/modelib-classification | MaxPool | false | 17,382 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c |
FusionConcat | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
class _NewEmptyTensorOp(torch.autograd.Function):
@staticmethod
def forward(ctx, x, new_shape):
ctx.shape = x.shape
return x.new_empty(new_shape)
@staticmethod
def backward(ctx, gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | Singingkettle/SAF-FCOS | FusionConcat | false | 18,381 | [
"BSD-2-Clause"
] | 10 | 5d00b83d659552940025923460d02bb2db7d29e8 | https://github.com/Singingkettle/SAF-FCOS/tree/5d00b83d659552940025923460d02bb2db7d29e8 |
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