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 |
|---|---|---|---|---|---|---|---|---|---|---|
FFN | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class FFN(nn.Module):
def __init__(self, d_model, d_ffn, dropout=0):
super().__init__()
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = F.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Tarandro/MOTR | FFN | false | 14,558 | [
"MIT"
] | 191 | f2bcc2df0b3bd959208e78c54a3e9d8a3434f9f4 | https://github.com/Tarandro/MOTR/tree/f2bcc2df0b3bd959208e78c54a3e9d8a3434f9f4 |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
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.... | liuruoze/mini-AlphaStar | DecoderLayer | false | 15,948 | [
"Apache-2.0"
] | 108 | cf9de2507d526a5fb8ef67676aab2ffb92738640 | https://github.com/liuruoze/mini-AlphaStar/tree/cf9de2507d526a5fb8ef67676aab2ffb92738640 |
TotalVariationLoss | import torch
import torch.nn as nn
class TotalVariationLoss(nn.Module):
def __init__(self):
super(TotalVariationLoss, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [b, 3, h, w].
It represents a RGB image with pixel values i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | TropComplique/CNNMRF | TotalVariationLoss | false | 18,011 | [
"MIT"
] | 3 | 602f861b14ed240acac89e6502e69f797d4f4a49 | https://github.com/TropComplique/CNNMRF/tree/602f861b14ed240acac89e6502e69f797d4f4a49 |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel=3):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size=kernel, padding=kernel //
2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Alpkant/CDCN | SpatialAttention | false | 8,892 | [
"MIT"
] | 0 | 4d4401824b8652a10739615e02e67148521739d2 | https://github.com/Alpkant/CDCN/tree/4d4401824b8652a10739615e02e67148521739d2 |
Skew | import torch
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Skew(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | MartinRenaudin/tutorials | Skew | false | 2,753 | [
"BSD-3-Clause"
] | 0 | 035d6827d77c52fed2a927f105e39fd73516f093 | https://github.com/MartinRenaudin/tutorials/tree/035d6827d77c52fed2a927f105e39fd73516f093 |
CanineAttention | # 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.... | Clemens123/transformers | CanineAttention | false | 13,225 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
MultiClassDiceLoss | # 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... | ivadomed-profile-analysis-project/ivadomed | MultiClassDiceLoss | false | 15,653 | [
"MIT"
] | 87 | 3b53e2cb2b210511943da439401e2471fd387876 | https://github.com/ivadomed-profile-analysis-project/ivadomed/tree/3b53e2cb2b210511943da439401e2471fd387876 |
SincFilter | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class SincFilter(torch_nn.Module):
""" SincFilter
Given the cut-off-frequency, produce the low-pass and high-pass
windowed-sinc-filters.
If input cut-off-frequency is (batchsize=1, signal_length, 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.triton_helpers import math as tl_math
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
as... | Ninushkat/Impact-Synth-Hardware | SincFilter | false | 14,116 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 |
TransformerBasicHead | # 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.... | Drill-D/SlowFast | TransformerBasicHead | false | 2,174 | [
"Apache-2.0"
] | 0 | d55ae1cf30a9415858a9bd5da983790a2b418653 | https://github.com/Drill-D/SlowFast/tree/d55ae1cf30a9415858a9bd5da983790a2b418653 |
UpConv | import torch
import torch.nn as nn
class UpConv(nn.Module):
def __init__(self, input_nc, output_nc, kernel_size):
super(UpConv, self).__init__()
self.deconv = nn.ConvTranspose2d(in_channels=input_nc, out_channels
=output_nc, kernel_size=2, bias=True, stride=2, padding=0)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | kkrish39/realtime-depth-prediction-from-monocular-videos | UpConv | false | 7,042 | [
"BSD-3-Clause"
] | 1 | 9cde9c1a6df6c91af1ada80b3aaeebae03fc59dc | https://github.com/kkrish39/realtime-depth-prediction-from-monocular-videos/tree/9cde9c1a6df6c91af1ada80b3aaeebae03fc59dc |
UnbalancedLoss | # 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... | Kwonyoung-Ryu/DeepGlobalRegistration | UnbalancedLoss | false | 11,613 | [
"MIT"
] | 0 | 0045118d96182047f4c09c4c4fe2a1b2b527cc5f | https://github.com/Kwonyoung-Ryu/DeepGlobalRegistration/tree/0045118d96182047f4c09c4c4fe2a1b2b527cc5f |
LayerScaling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Ajk4/online-normalization | LayerScaling | false | 8,826 | [
"BSD-3-Clause"
] | 0 | 84895855fb8b099ad8c1266dc325bec41d72ecf5 | https://github.com/Ajk4/online-normalization/tree/84895855fb8b099ad8c1266dc325bec41d72ecf5 |
scSE | import torch
import torch.nn as nn
class cSE(nn.Module):
def __init__(self, in_channels):
super().__init__()
reduced_filters = 1 if in_channels // 2 == 0 else in_channels // 2
self.global_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
self.pointwise_1 = nn.Conv2d(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
import torch.nn as nn
assert_... | mattroz/yatopi | scSE | false | 3,996 | [
"MIT"
] | 0 | 278bac6f3d2f13916ae9d43309b9f38b608426bd | https://github.com/mattroz/yatopi/tree/278bac6f3d2f13916ae9d43309b9f38b608426bd |
Encoder1 | # 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.... | MingSun-Tse/Collaborative-Distillation | Encoder1 | false | 14,018 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
PatchEmbedding | # 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 ... | aditya-agrawal-30502/vformer | PatchEmbedding | false | 14,747 | [
"MIT"
] | 90 | e1f4950f980238442ff1dc39a8f0791e4fbc9dac | https://github.com/aditya-agrawal-30502/vformer/tree/e1f4950f980238442ff1dc39a8f0791e4fbc9dac |
MeanPoolWithMask | import torch
from torch import nn
import torch.utils.data
class MeanPoolWithMask(nn.Module):
def __init__(self):
super(MeanPoolWithMask, self).__init__()
self.inf = 10000000000000.0
def forward(self, tensor, mask, dim=0):
masks = mask.view(mask.size(0), mask.size(1), -1).float()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | LindaCY/fastNLP | MeanPoolWithMask | false | 17,613 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
EmbedComp | # 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.optim
import torch.utils.data
import torch.ba... | Divyanshu23/model-zoo | EmbedComp | false | 8,087 | [
"MIT"
] | 43 | 2eea6df691d302e182bb1ff8ec5af3542de562ba | https://github.com/Divyanshu23/model-zoo/tree/2eea6df691d302e182bb1ff8ec5af3542de562ba |
SoftCrossEntropyLoss | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
import torch.nn.functional as F
class SoftCrossEntropyLoss(nn.Module):
"""
Calculate the CrossEntropyLoss with soft targets
:param weight: Weight to assign to each of the classes. Default: None
:type weight: list of f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Lis... | SenWu/fonduer | SoftCrossEntropyLoss | false | 5,811 | [
"MIT"
] | 1 | c4f8d95cec97552b34412c6787eb7370ae17424f | https://github.com/SenWu/fonduer/tree/c4f8d95cec97552b34412c6787eb7370ae17424f |
ScaledDotProductAttention | import torch
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Attention mechansims usually scale values based on relationships between
keys and queries.
Attention(Q,K,V) = A(Q,K)*V where A() is a normalization function.
A common choice for the normalization function is s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KalleBylin/tft_webapp | ScaledDotProductAttention | false | 9,227 | [
"Apache-2.0"
] | 0 | 008f109e77f8bada417655dab482f340adb8cb6b | https://github.com/KalleBylin/tft_webapp/tree/008f109e77f8bada417655dab482f340adb8cb6b |
AttnConnector | # 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 ... | ruinunca/NeuralDialog-ZSDG | AttnConnector | false | 16,352 | [
"Apache-2.0"
] | 132 | c20359541036ea876a126d1c7c172b820476dcb2 | https://github.com/ruinunca/NeuralDialog-ZSDG/tree/c20359541036ea876a126d1c7c172b820476dcb2 |
maxout | import torch
import torch.nn as nn
import torch.utils.data
class maxout(nn.Module):
"""
maxout network
"""
def __init__(self, in_feature, out_feature, pool_size):
super(maxout, self).__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.pool_size ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Angelinaa/KOBE | maxout | false | 45 | [
"MIT"
] | 0 | 4d25487051e2791a977e59297f70a25e51806466 | https://github.com/Angelinaa/KOBE/tree/4d25487051e2791a977e59297f70a25e51806466 |
C3 | import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(32, 64,
kernel_size=(3, 3), bias=32)), ('relu3', nn.ReLU())]))
def forward(self, img):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | devillove084/DeepSignal | C3 | false | 12,260 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
CRF | # 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... | lzx325/NCRF | CRF | false | 12,757 | [
"Apache-2.0"
] | 0 | 2fc081184e3bc45b043e4c8c0a94644a0149e54c | https://github.com/lzx325/NCRF/tree/2fc081184e3bc45b043e4c8c0a94644a0149e54c |
LovaszLoss | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from itertools import filterfalse
def jaccard(outputs, targets, per_image=False, non_empty=False, min_pixels=5):
batch_size = outputs.size()[0]
eps = 0.001
if not per_image:
batch_size = 1
dice_target = target... | 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 numpy as np
from torch import nn
import torch.nn.functional as F
from itertools im... | kevinkwshin/kaggle-pneumothorax | LovaszLoss | false | 16,124 | [
"MIT"
] | 74 | 24b91a9425097023f0cc7781a9380cb247babe22 | https://github.com/kevinkwshin/kaggle-pneumothorax/tree/24b91a9425097023f0cc7781a9380cb247babe22 |
Decoder | # 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... | Markussorensen/mlops_exercises | Decoder | false | 2,633 | [
"Apache-2.0"
] | 0 | 52a3198367b66bbe0a5cfdc7a9424789b03273db | https://github.com/Markussorensen/mlops_exercises/tree/52a3198367b66bbe0a5cfdc7a9424789b03273db |
Allocation | # 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.... | pjordan/dmch | Allocation | false | 4,120 | [
"Apache-2.0"
] | 0 | 84e04ddb0679007b15acfdc275e0e3f51e50d9f2 | https://github.com/pjordan/dmch/tree/84e04ddb0679007b15acfdc275e0e3f51e50d9f2 |
Sparsemax | from torch.autograd import Function
import torch
import torch.nn as nn
def _make_ix_like(X, dim):
d = X.size(dim)
rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype)
view = [1] * X.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def _roll_last(X, dim):
if dim == -1:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._... | roholazandie/entmax | Sparsemax | false | 7,587 | [
"MIT"
] | 1 | 657374e6a792ec6840b6f78bc759cc1f51570aad | https://github.com/roholazandie/entmax/tree/657374e6a792ec6840b6f78bc759cc1f51570aad |
RobertaClassificationHead | # 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... | HebatallaTarek/Empathy-Mental-Health | RobertaClassificationHead | false | 15,677 | [
"BSD-3-Clause"
] | 66 | 16e2a5f93aabd22803bb39805f8e76c8bea0ccf2 | https://github.com/HebatallaTarek/Empathy-Mental-Health/tree/16e2a5f93aabd22803bb39805f8e76c8bea0ccf2 |
ResBlock | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class ResBlock(nn.Module):
def __init__(self, num_of_channels):
super(ResBlock, self).__init__()
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.... | JinYAnGHe/openvino_training_extensions | ResBlock | false | 2,717 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
adder2d | from torch.autograd import Function
import math
import torch
import torch.nn as nn
def adder2d_function(X, W, stride=1, padding=0):
n_filters, _d_filter, h_filter, w_filter = W.size()
n_x, _d_x, h_x, w_x = X.size()
h_out = (h_x - h_filter + 2 * padding) / stride + 1
w_out = (w_x - w_filter + 2 * paddi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.autograd import Function
import math
import torch.nn as nn
ass... | mark531593296/AdderNet | adder2d | false | 10,443 | [
"BSD-3-Clause"
] | 0 | 2936728f537c0cceb8a47727630e5723af86df61 | https://github.com/mark531593296/AdderNet/tree/2936728f537c0cceb8a47727630e5723af86df61 |
AlphaEntropy | # 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... | Gromy1211/torch-light | AlphaEntropy | false | 11,455 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
def forward(self, x):
m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Adelashl6/mask_transformers | LayerNorm | false | 4,804 | [
"MIT"
] | 1 | 2a2e4d1b40ae3ed546cb850d041af246806b63e7 | https://github.com/Adelashl6/mask_transformers/tree/2a2e4d1b40ae3ed546cb850d041af246806b63e7 |
VarianceLayer | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | GuYuanjie/DeepFusionPrior | VarianceLayer | false | 5,227 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
_MultipleInputNetwork | # 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_... | Yifanfanfanfan/torchutils | _MultipleInputNetwork | false | 18,129 | [
"MIT"
] | 9 | 939331d28fcee97bfb0a4b2eaab8e799877fb0dc | https://github.com/Yifanfanfanfan/torchutils/tree/939331d28fcee97bfb0a4b2eaab8e799877fb0dc |
TuckERLoss | # 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
... | CogNLP/CogKGE | TuckERLoss | false | 5,016 | [
"MIT"
] | 1 | 70d851d6489600c1e90eb25b0388a3ceba2f078c | https://github.com/CogNLP/CogKGE/tree/70d851d6489600c1e90eb25b0388a3ceba2f078c |
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.... | ngbsLab/Korean-Speech-Recognition | MultiHeadAttention | false | 7,344 | [
"Apache-2.0"
] | 1 | 3867bf7d23222da6812c9b98a93d3c6f7b3c80fc | https://github.com/ngbsLab/Korean-Speech-Recognition/tree/3867bf7d23222da6812c9b98a93d3c6f7b3c80fc |
disparityregression | # 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 numpy as np
from torch import nn
import torch.utils.data
from torch.autograd import Variable
import torch.nn.parallel
import torch.ut... | Sarah20187/X-StereoLab | disparityregression | false | 14,453 | [
"MIT"
] | 192 | 9ae8c1413307e7df91b14a7f31e8a95f9e5754f9 | https://github.com/Sarah20187/X-StereoLab/tree/9ae8c1413307e7df91b14a7f31e8a95f9e5754f9 |
FCVAE | import torch
from torch.nn import functional as F
from torch import nn
class BaseVAE(nn.Module):
"""
Base abstract class for the Variational Autoencoders
"""
def __init__(self, channels=1, width=28, height=28, z_dim=2):
"""
Constructor
Parameters:
channels - The n... | 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... | mbusy/vae | FCVAE | false | 7,201 | [
"MIT"
] | 1 | 455e382a557b72fc944460331e5dd010ff83a76a | https://github.com/mbusy/vae/tree/455e382a557b72fc944460331e5dd010ff83a76a |
ClassAttentionBlock | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, hidden_dim, out_dim=None) ->None:
super().__init__()
out_dim = out_dim or dim
self.fc1 = nn.Linear(dim, hidden_dim)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_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
from torch._inductor.runtime.... | alhamami/Object-Detection-And-Tracking | ClassAttentionBlock | false | 18,320 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
Critic | 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 Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import tor... | david-varela/collaboration_and_competition | Critic | false | 12,250 | [
"MIT"
] | 0 | a170cc02eb3917af19d6aafa8b37f6089b83c35f | https://github.com/david-varela/collaboration_and_competition/tree/a170cc02eb3917af19d6aafa8b37f6089b83c35f |
NormalIsotropicCovarianceLayer | import abc
import math
import torch
class ProbabilisticLayer(torch.nn.Module, metaclass=abc.ABCMeta):
"""Probabilistic layer to be used by the encoder/decoder of a
Variational AutoEncoder.
"""
@abc.abstractmethod
def forward(self, inputs):
"""Compute the parameters of the distribution co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | bolajiy/beer | NormalIsotropicCovarianceLayer | false | 14,969 | [
"MIT"
] | 46 | 6fe968c7ca4864437890aa6bd705755c2580696e | https://github.com/bolajiy/beer/tree/6fe968c7ca4864437890aa6bd705755c2580696e |
Swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | klaudiapalasz/torchlayers | Swish | false | 15,843 | [
"MIT"
] | 573 | e6edd8797875325b7c0539d75a12f0d51f494127 | https://github.com/klaudiapalasz/torchlayers/tree/e6edd8797875325b7c0539d75a12f0d51f494127 |
PixelNormLayer | import torch
import torch.nn as nn
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.epsilon = epsilon
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, k... | 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_... | CV-IP/interfacegan | PixelNormLayer | false | 13,441 | [
"MIT"
] | 855 | 5a556b8e693f6e1888f769f653aaafaaccca5dc2 | https://github.com/CV-IP/interfacegan/tree/5a556b8e693f6e1888f769f653aaafaaccca5dc2 |
VitMlpHead | import torch
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True, help=
'Path to input JSON')
group.add_argument('--json-keys', nargs='+', default=['text'], help=
'space separate ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | sourcery-ai-bot/Megatron-LM | VitMlpHead | false | 4,377 | [
"MIT"
] | 0 | f27f44e2c49d1cb39b2288bef6f7d837e11094cb | https://github.com/sourcery-ai-bot/Megatron-LM/tree/f27f44e2c49d1cb39b2288bef6f7d837e11094cb |
LinearModel | import torch
import torch.utils.data
import torch.nn
import torch.optim
class LinearModel(torch.nn.Module):
def __init__(self, _in, out):
super(LinearModel, self).__init__()
self.input = torch.nn.Linear(_in, _in)
self.hidden_1 = torch.nn.Linear(_in, out)
self.hidden_2 = torch.nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.optim
assert_size_stride = ... | ajsampathk/trt_pose | LinearModel | false | 18,234 | [
"MIT"
] | 7 | 592e038cacaf43b6a502b759a035a4e7cae9db9e | https://github.com/ajsampathk/trt_pose/tree/592e038cacaf43b6a502b759a035a4e7cae9db9e |
SqueezeExcite | import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms i... | JaminFong/dali-pytorch | SqueezeExcite | false | 8,330 | [
"Apache-2.0"
] | 41 | 7bd5d2380d210a32d24c7309da69c8d2c5db8759 | https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759 |
LinearZeros | # 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.... | Eladhi/VI_Glow | LinearZeros | false | 5,122 | [
"MIT"
] | 1 | 9c48fbf8fa10c81fc2354a07fcc2837a77d06cef | https://github.com/Eladhi/VI_Glow/tree/9c48fbf8fa10c81fc2354a07fcc2837a77d06cef |
Block | # 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.... | xuewengeophysics/Conformer | Block | false | 13,141 | [
"Apache-2.0"
] | 0 | e769a1ac9ab110dae2a356a4de1e06ccd0e95041 | https://github.com/xuewengeophysics/Conformer/tree/e769a1ac9ab110dae2a356a4de1e06ccd0e95041 |
CA_Block | import torch
import torch.nn as nn
class CA_Block(nn.Module):
def __init__(self, in_dim):
super(CA_Block, self).__init__()
self.chanel_in = in_dim
self.gamma = nn.Parameter(torch.ones(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Mhaiyang/CVPR2021_PFNet | CA_Block | false | 8,556 | [
"BSD-3-Clause"
] | 24 | 2c4cab0730e6a0619fad79092f0b34f71c3b56c4 | https://github.com/Mhaiyang/CVPR2021_PFNet/tree/2c4cab0730e6a0619fad79092f0b34f71c3b56c4 |
InvGridSamplerNumerator | # 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 numpy as np
fro... | EasyTry/coordinate_based_inpainting | InvGridSamplerNumerator | false | 8,054 | [
"MIT"
] | 13 | cbe0e3a58c8cb2054f0536a56f57264fd9967d63 | https://github.com/EasyTry/coordinate_based_inpainting/tree/cbe0e3a58c8cb2054f0536a56f57264fd9967d63 |
PA_UP | # 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... | YingqiLiulll/scrips_for_SR | PA_UP | false | 1,288 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ImportPaddle/APCNet | DiceLoss | false | 2,369 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
ElectronicAsymptotic | import torch
from torch import nn
class ElectronicAsymptotic(nn.Module):
"""Jastrow factor with a correct electronic cusp.
The Jastrow factor is calculated from distances between all pairs of
electrons, :math:`d_{ij}`,
.. math::
\\mathrm \\gamma
:=\\sum_{ij}-\\frac{c}{\\alpha(1+\\alp... | 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... | MikeEntwistle/deepqmc | ElectronicAsymptotic | false | 17,730 | [
"MIT"
] | 4 | b5c20bf1768f04227becd5079c6b40aefc97d26c | https://github.com/MikeEntwistle/deepqmc/tree/b5c20bf1768f04227becd5079c6b40aefc97d26c |
Downsample | # 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... | javierrodenas/clearml_javi | Downsample | false | 10,358 | [
"Apache-2.0"
] | 0 | b6326104fe6a6f522223c2ac3d87468990a9e6f2 | https://github.com/javierrodenas/clearml_javi/tree/b6326104fe6a6f522223c2ac3d87468990a9e6f2 |
FocalLoss | import torch
import torch.nn.functional as F
from torch import nn as nn
class FocalLoss(nn.Module):
"""Focal loss function for imbalanced dataset.
Args:
alpha (float): weighing factor between 0 and 1. Alpha may be set by inverse
class frequency
gamma (float): modulati... | 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 ... | marshuang80/pe-slice-finder | FocalLoss | false | 7,163 | [
"Apache-2.0"
] | 1 | 2426a55c404e8eb694110351d604d6bdd613e5ae | https://github.com/marshuang80/pe-slice-finder/tree/2426a55c404e8eb694110351d604d6bdd613e5ae |
Net1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Yixiao99/deep-learning-containers | Net1 | false | 14,702 | [
"Apache-2.0"
] | 383 | 01f078adf5abfb92e802b326511981bdd4a8c85c | https://github.com/Yixiao99/deep-learning-containers/tree/01f078adf5abfb92e802b326511981bdd4a8c85c |
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.... | cclauss/nonauto-nmt | EncoderLayer | false | 15,022 | [
"BSD-3-Clause"
] | 262 | efcbe4f2329b140ac3ce06abb6409457cebc8e49 | https://github.com/cclauss/nonauto-nmt/tree/efcbe4f2329b140ac3ce06abb6409457cebc8e49 |
MultiscalePixelLoss | import torch
import torch.nn as nn
class MultiscalePixelLoss(nn.Module):
def __init__(self, loss_f=nn.L1Loss(), scale=5):
super(MultiscalePixelLoss, self).__init__()
self.criterion = loss_f
self.downsample = nn.AvgPool2d(2, stride=2, count_include_pad=False)
self.weights = [1, 0.5... | 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
... | grofit/traiNNer | MultiscalePixelLoss | false | 15,510 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch._utils
import torch... | Alicegaz/torchok | GELU | false | 16,930 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | BUPT-PRIV/detectron2 | IndepAnisotropicGaussianUVLoss | false | 11,239 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
RMSPE | import torch
import torch.nn as nn
class RMSPE(nn.Module):
def __init__(self, eps: 'float'=1e-08):
super().__init__()
self.eps = eps
def forward(self, pred: 'torch.Tensor', target: 'torch.Tensor'):
return torch.sqrt(torch.mean(torch.square((pred - target).abs() / (
target... | 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... | Phimos/SIGSPATIAL-2021-GISCUP-3rd-Solution | RMSPE | false | 8,648 | [
"MIT"
] | 11 | 79fcf9941c28cdb2eb38a3654e1514a1d998a41c | https://github.com/Phimos/SIGSPATIAL-2021-GISCUP-3rd-Solution/tree/79fcf9941c28cdb2eb38a3654e1514a1d998a41c |
GAT | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttention(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttention, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | new2scala/graph-cnn.pytorch | GAT | false | 16,191 | [
"MIT"
] | 330 | 8bee0c2ed687dcfdb277c71b70c8ea747b6ca9c7 | https://github.com/new2scala/graph-cnn.pytorch/tree/8bee0c2ed687dcfdb277c71b70c8ea747b6ca9c7 |
QueryModule | import torch
from torch import nn
from torch.nn import functional as F
class QueryModule(nn.Module):
"""
A neural module that takes as input a feature map and an attention and produces a feature
map as output.
Extended Summary
----------------
A :class:`QueryModule` takes a feature map and an... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | kdexd/probnmn-clevr | QueryModule | false | 15,793 | [
"MIT"
] | 69 | 9c1b2286cf30e9fb045370153c9242a39760e02e | https://github.com/kdexd/probnmn-clevr/tree/9c1b2286cf30e9fb045370153c9242a39760e02e |
AvgPoolHead | import torch
import torch.nn as nn
import torch.optim
class AvgPoolHead(nn.Module):
def __init__(self, in_channels, out_channels, fea_map_size):
super(AvgPoolHead, self).__init__()
self.avgpool = nn.AvgPool2d(fea_map_size, stride=1)
self.fc = nn.Linear(in_channels, out_channels)
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
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | NiteshBharadwaj/structured_aleatoric_uncertainty_for_human_pose | AvgPoolHead | false | 905 | [
"MIT"
] | 0 | c74fb7384be562f0a0f1966b3fadf19e13a235f2 | https://github.com/NiteshBharadwaj/structured_aleatoric_uncertainty_for_human_pose/tree/c74fb7384be562f0a0f1966b3fadf19e13a235f2 |
InnerProductNetwork | import torch
import torch.utils.data
class InnerProductNetwork(torch.nn.Module):
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
num_fields = x.shape[1]
row, col = list(), list()
for i in range(num_fields - 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | Fanxingye/Autotabular | InnerProductNetwork | false | 5,150 | [
"Apache-2.0"
] | 1 | d630c78290a52f8c73885afb16884e18135c34f6 | https://github.com/Fanxingye/Autotabular/tree/d630c78290a52f8c73885afb16884e18135c34f6 |
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.... | M4rt1nM4yr/recipient_line_detection_DAS22 | TransformerDecoderLayer | false | 823 | [
"MIT"
] | 0 | be5ed87940ff2c2740cf86130743538a2ba6ac4b | https://github.com/M4rt1nM4yr/recipient_line_detection_DAS22/tree/be5ed87940ff2c2740cf86130743538a2ba6ac4b |
ClassificationModel | # 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_... | CraigWang1/EfficientDet-PyTorch | ClassificationModel | false | 13,567 | [
"Apache-2.0"
] | 66 | 531d3c83338f03aa5c6f0615839c0ea5c03025f6 | https://github.com/CraigWang1/EfficientDet-PyTorch/tree/531d3c83338f03aa5c6f0615839c0ea5c03025f6 |
MultiHeadAttention | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class MultiHeadAttention(nn.Module):
def __init__(self, channels, out_channels, n_heads, p_dropout=0.0,
window_size=None, heads_share=True, block_length=None,
proximal_bias=False, proximal_i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | roedoejet/vits | MultiHeadAttention | false | 10,804 | [
"MIT"
] | 0 | 982e3632c876562563bc74c37d485eaf53715ecc | https://github.com/roedoejet/vits/tree/982e3632c876562563bc74c37d485eaf53715ecc |
QuanConv | from torch.autograd import Function
import torch
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def quantize(input, nbit):
return Quantizer.apply(input, nbit)
def dorefa_a(input, nbit_a):
return quantize(torch.clamp(0.1 * input, 0, 1), nbit_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jzz24/pytorch_quantization | QuanConv | false | 13,960 | [
"MIT"
] | 71 | 0c2d93c8ce4f85dd2c34ea6f36c58d14db21bf8e | https://github.com/Jzz24/pytorch_quantization/tree/0c2d93c8ce4f85dd2c34ea6f36c58d14db21bf8e |
AUXModule | # 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_... | HamzaFarhan/segmentation_models.pytorch | AUXModule | false | 11,473 | [
"MIT"
] | 0 | b7803df1d17027f329e267ba4c55144adfdd4da9 | https://github.com/HamzaFarhan/segmentation_models.pytorch/tree/b7803df1d17027f329e267ba4c55144adfdd4da9 |
Gradient | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
class Gradient(nn.Module):
def __init__(self):
super(Gradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
kernel_h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | RunqiuBao/Event_ESTRNN | Gradient | false | 14,339 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
ScaledDotProductAttentionMemory | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionali... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CurryYuan/X-Trans2Cap | ScaledDotProductAttentionMemory | false | 7,951 | [
"Apache-2.0"
] | 11 | c78a27209f14fcbbec74fe8b5edc06faea2e7d44 | https://github.com/CurryYuan/X-Trans2Cap/tree/c78a27209f14fcbbec74fe8b5edc06faea2e7d44 |
SmoothL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CityU-AIM-Group/HTD | SmoothL1Loss | false | 17,115 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
DiceLoss | import torch
class DiceLoss(torch.nn.Module):
def __init__(self, weight=None, size_average=True, per_image=False, eps
=1e-06):
super().__init__()
self.size_average = size_average
self.register_buffer('weight', weight)
self.per_image = per_image
self.eps = 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | sebasmos/Spacenet7TRDP | DiceLoss | false | 12,955 | [
"Apache-2.0"
] | 0 | 03b5819321108017f8f8c2d359264c8e18d9e38a | https://github.com/sebasmos/Spacenet7TRDP/tree/03b5819321108017f8f8c2d359264c8e18d9e38a |
ConvInRelu | import torch
import numpy as np
from torch import nn
import torch.onnx
class ConvInRelu(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride=1):
super(ConvInRelu, self).__init__()
self.n_params = 0
self.channels = channels_out
self.reflection_pad = nn.Refl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JuanFuriaz/donkey_share | ConvInRelu | false | 11,608 | [
"MIT"
] | 0 | caad831ca21094f05f9084f881ca3bbfa4168e4c | https://github.com/JuanFuriaz/donkey_share/tree/caad831ca21094f05f9084f881ca3bbfa4168e4c |
LaplacianPyramidLayer | import torch
from typing import Tuple
import torch.nn as nn
from torch.nn import functional as F
class PyramidDown(nn.Module):
def __init__(self) ->None:
super(PyramidDown, self).__init__()
self.filter = nn.Parameter(torch.tensor([[1, 4, 6, 4, 1], [4, 16,
24, 16, 4], [6, 24, 36, 24, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn import functional as F
assert_size_stride = ... | masanorihirano/pytorch_extra_mhirano | LaplacianPyramidLayer | false | 7,174 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
ClsCriterion | import torch
import torch.nn as nn
class ClsCriterion(nn.Module):
def __init__(self):
super(ClsCriterion, self).__init__()
def forward(self, predict, label, batch_weight=None):
"""
:param predict: B*C log_softmax result
:param label: B*C one-hot label
:param batch_wei... | 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... | PaperCodeSubmission/ICML2020-697 | ClsCriterion | false | 8,661 | [
"MIT"
] | 12 | 00f7732c236b9c6234e76a47dfebe5de314d5c01 | https://github.com/PaperCodeSubmission/ICML2020-697/tree/00f7732c236b9c6234e76a47dfebe5de314d5c01 |
SimpleASinModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleASinModule | false | 14,643 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
DilConv1dWithGLU | # 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 ... | Napkin-DL/my-aws-example | DilConv1dWithGLU | false | 9,343 | [
"MIT-0"
] | 0 | c6e8a1ec60468938c259fcec7542c85f5464c898 | https://github.com/Napkin-DL/my-aws-example/tree/c6e8a1ec60468938c259fcec7542c85f5464c898 |
GCN | from torch.nn import Module
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CVIR/CoMix | GCN | false | 7,907 | [
"Apache-2.0"
] | 13 | 593b5b3ba6e060018e4b55ab288dab71c2ee2e18 | https://github.com/CVIR/CoMix/tree/593b5b3ba6e060018e4b55ab288dab71c2ee2e18 |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | akashkmr27089/ReinforcementLearning_Udacity_Deep_Reinforcemnt_Learning | Policy | false | 3,083 | [
"MIT"
] | 0 | b7dc13b0116898848d8d0b8a95b7af182982bd6b | https://github.com/akashkmr27089/ReinforcementLearning_Udacity_Deep_Reinforcemnt_Learning/tree/b7dc13b0116898848d8d0b8a95b7af182982bd6b |
BinaryMin | # 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 abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMin | false | 11,572 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
PinballLoss | import torch
import torch.nn as nn
class PinballLoss(nn.Module):
""" Pinball Loss
Computes the pinball loss between y and y_hat.
Parameters
----------
y: tensor
actual values in torch tensor.
y_hat: tensor (same shape as y)
predicted values in torch tensor.
tau: float, between 0 and 1
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | venkatkorapaty/esrnn | PinballLoss | false | 11,005 | [
"MIT"
] | 0 | 411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c | https://github.com/venkatkorapaty/esrnn/tree/411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c |
BasicModel_ConvNet_One_Conv | # 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_... | ngduduong/captum | BasicModel_ConvNet_One_Conv | false | 4,080 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
ContinuousActor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | pjordan/rlcc | ContinuousActor | false | 7,474 | [
"Apache-2.0"
] | 1 | e84b8b5c14680dbad2efae22756fb40606b2384a | https://github.com/pjordan/rlcc/tree/e84b8b5c14680dbad2efae22756fb40606b2384a |
NormalizeOutput | import torch
import torch.nn.functional as F
from torch import nn
import torch.optim
class NormalizeOutput(nn.Module):
"""
Module that scales the input tensor to the unit norm w.r.t. the specified axis.
Actually, the module analog of `torch.nn.functional.normalize`
"""
def __init__(self, dim=1, 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 libdevice
from torch import nn
import ... | agermanidis/HiDT | NormalizeOutput | false | 18,232 | [
"BSD-3-Clause"
] | 4 | 69192bb26785fc4e05038c45d05f2f880dd362d0 | https://github.com/agermanidis/HiDT/tree/69192bb26785fc4e05038c45d05f2f880dd362d0 |
L2Norm | import torch
from itertools import product as product
import torch.nn as nn
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
self.eps = 1e-10
... | 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 itertools import product as product
import torch.nn as nn
import torch.nn.... | AlanSavio25/AVSR-Dataset-Pipeline | L2Norm | false | 18,407 | [
"MIT"
] | 2 | 6e6d44eca6133c2e0223e9be8d011be0b68c73d1 | https://github.com/AlanSavio25/AVSR-Dataset-Pipeline/tree/6e6d44eca6133c2e0223e9be8d011be0b68c73d1 |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.optim
assert_size_stride = ... | tucachmo2202/trt_pose | CmapPafHeadAttention | false | 13,082 | [
"MIT"
] | 0 | b847fc197c32219dc2d719c2b42906603da0988a | https://github.com/tucachmo2202/trt_pose/tree/b847fc197c32219dc2d719c2b42906603da0988a |
TensorClamp | # 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... | Akababa/torch2trt | TensorClamp | false | 18,427 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
NormalizedGramMatrix | import torch
import torch.nn as nn
def normalize_by_stddev(tensor):
"""
divides channel-wise by standard deviation of channel
"""
channels = tensor.shape[1]
stddev = tensor.std(dim=(0, 2)).view(1, channels, 1) + 1e-15
return tensor.div(stddev)
class NormalizedGramMatrix(nn.Module):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ChuckHend/nst-zoo | NormalizedGramMatrix | false | 2,113 | [
"MIT"
] | 0 | 130e485289c5a9417c3dc36980b87373f12f3697 | https://github.com/ChuckHend/nst-zoo/tree/130e485289c5a9417c3dc36980b87373f12f3697 |
LayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | amazon-research/network-deconvolution-pp | LayerNorm | false | 18,347 | [
"Apache-2.0"
] | 6 | 99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | https://github.com/amazon-research/network-deconvolution-pp/tree/99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce |
PositionWiseFeedForwardNetworks | # 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... | L-Zhe/FasySeq | PositionWiseFeedForwardNetworks | false | 8,417 | [
"Apache-2.0"
] | 34 | 2cd2abd290666b1e118d8ad11c973b58ca4f0573 | https://github.com/L-Zhe/FasySeq/tree/2cd2abd290666b1e118d8ad11c973b58ca4f0573 |
StepRankerLogistic | import torch
from torch import nn
class StepRankerLogistic(nn.Module):
"""a logistic ranker"""
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super(StepRankerLogistic, self).__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YilunZhou/wikihow-embedding | StepRankerLogistic | false | 18,140 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 |
MaskedMSELoss | import torch
import torch.nn as nn
class MaskedMSELoss(nn.Module):
def __init__(self):
super(MaskedMSELoss, self).__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len
target -> batch*seq_len
mask -... | 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... | RaleLee/conv-emotion | MaskedMSELoss | false | 11,825 | [
"MIT"
] | 0 | 1b07223cbdfd52eb31e913e982d18ff1ed3daf08 | https://github.com/RaleLee/conv-emotion/tree/1b07223cbdfd52eb31e913e982d18ff1ed3daf08 |
VarianceC | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | caixin1998/pl-template | VarianceC | false | 10,112 | [
"BSD-3-Clause"
] | 0 | 6918f0289ab2b32d107e5722617d25c9a683399c | https://github.com/caixin1998/pl-template/tree/6918f0289ab2b32d107e5722617d25c9a683399c |
CCAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Softmax
from torchvision.transforms import *
class CCAMDec(Module):
"""
CCAM decoding module
"""
def __init__(self):
super(CCAMDec, self).__init__()
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ruijieren98/DANet | CCAMDec | false | 16,359 | [
"MIT"
] | 2,190 | e38d61e371179833c08888fd5a1ee444cf5bd875 | https://github.com/ruijieren98/DANet/tree/e38d61e371179833c08888fd5a1ee444cf5bd875 |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
import torch
import torch.nn as nn
import to... | bomtorazek/contrastive-unpaired-translation | ToRGB | false | 12,203 | [
"BSD-3-Clause"
] | 0 | 07c048038375e1b9a4e464154b8dbc49f5e16ede | https://github.com/bomtorazek/contrastive-unpaired-translation/tree/07c048038375e1b9a4e464154b8dbc49f5e16ede |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not)
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __in... | 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... | LTTBasic/lecttue-diagonosis | AconC | false | 749 | [
"MIT"
] | 0 | a9573f79da1fa8dcdd649bfd819ffad67ecad309 | https://github.com/LTTBasic/lecttue-diagonosis/tree/a9573f79da1fa8dcdd649bfd819ffad67ecad309 |
Conv2dBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | MattAlexMiracle/SmartPatch | Conv2dBlock | false | 17,690 | [
"MIT"
] | 7 | c485cb433d8e085d6eae10a335ee19f5e6c1a41c | https://github.com/MattAlexMiracle/SmartPatch/tree/c485cb433d8e085d6eae10a335ee19f5e6c1a41c |
adder2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.autograd import Function
import math
import torch.nn as nn
ass... | Xyfuture/AdderNet | adder2d | false | 11,992 | [
"BSD-3-Clause"
] | 0 | 62f567164175558622748464fb2f47d37d579b29 | https://github.com/Xyfuture/AdderNet/tree/62f567164175558622748464fb2f47d37d579b29 |
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