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 |
|---|---|---|---|---|---|---|---|---|---|---|
MiniBatchStdDev | import torch
import torch.nn as nn
class MiniBatchStdDev(nn.Module):
"""Layer that appends to every element of a batch a new ftr map containing the std of its group."""
def __init__(self, group_sz=4, unbiased_std=False):
super().__init__()
self.group_sz = group_sz
self.unbiased_std = ... | 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_... | davidleonfdez/face2anime | MiniBatchStdDev | false | 1,805 | [
"MIT"
] | 0 | 896bf85a7aa28322cc9e9e586685db8cbbf39d89 | https://github.com/davidleonfdez/face2anime/tree/896bf85a7aa28322cc9e9e586685db8cbbf39d89 |
TensorProba | import torch
class TensorProba(torch.nn.Module):
def __init__(self, dim=1):
self.dim = dim
super().__init__()
def forward(self, input):
total = torch.sum(input, dim=self.dim, keepdim=True)
return input / total
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ge... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorProba | false | 14,052 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
ada_mask | # 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 ... | NJUVISION/AWnet | ada_mask | false | 8,659 | [
"MIT"
] | 16 | f47a1692819a778b513b882d36ed727f7732d37b | https://github.com/NJUVISION/AWnet/tree/f47a1692819a778b513b882d36ed727f7732d37b |
AttLuong | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttLuong(torch.nn.Module):
"""
AttLuong: Attention according to Luong that can be used by the
Alignment module.
"""
def __init__(self, q_dim, y_dim, softmax=True):
super().__init__()
self.q_dim = q_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.... | ishine/NISQA | AttLuong | false | 15,639 | [
"MIT"
] | 223 | 2c8917f30c4e4bbca3a48e9852301f1e2480a741 | https://github.com/ishine/NISQA/tree/2c8917f30c4e4bbca3a48e9852301f1e2480a741 |
EqualLinear | from torch.autograd import Function
import math
import torch
from torch.nn import functional as F
from torch import nn
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
class FusedLeakyReLUFunctionBackward(Function):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
from torch import nn
assert_size... | BillyXYB/TransEditor | EqualLinear | false | 17,068 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
Mfm | # 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_... | githubhjx/Deep-Learning- | Mfm | false | 12,447 | [
"Apache-2.0"
] | 0 | 5a22fb5696d930ed334aa1cbf2b213956b1c7026 | https://github.com/githubhjx/Deep-Learning-/tree/5a22fb5696d930ed334aa1cbf2b213956b1c7026 |
GlobalAvgPool | # 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... | Fork-for-Modify/VideoFeatureExtractor | GlobalAvgPool | false | 8,101 | [
"Apache-2.0"
] | 15 | a73bb5a575a318c2d71bc8dd2432c8941c35a77f | https://github.com/Fork-for-Modify/VideoFeatureExtractor/tree/a73bb5a575a318c2d71bc8dd2432c8941c35a77f |
Multi_Head_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.triton_helpers import libdevice, math as tl_math
im... | Moon-xm/Chinese-Text-Classification-Pytorch | Multi_Head_Attention | false | 11,725 | [
"MIT"
] | 0 | 19fe64006418bf4296f884e4d1f038c17b34d3de | https://github.com/Moon-xm/Chinese-Text-Classification-Pytorch/tree/19fe64006418bf4296f884e4d1f038c17b34d3de |
LearnMaskedDefault | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the defaul... | 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.data
import torch.nn
assert_size_stride = torch.... | kevinmtian/pytorchvideo | LearnMaskedDefault | false | 15,824 | [
"Apache-2.0"
] | 2,391 | 168e16859a6029ef8ebeb476f9163bebb6c6b87d | https://github.com/kevinmtian/pytorchvideo/tree/168e16859a6029ef8ebeb476f9163bebb6c6b87d |
ReduceMax | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.asse... | mil-tokyo/webdnn | ReduceMax | false | 16,083 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
LeNet300 | import torch
from torch import nn
class LeNet300(nn.Module):
def __init__(self):
super(LeNet300, self).__init__()
self.fc1 = nn.Linear(784, 300, bias=True)
self.r1 = nn.ReLU()
self.fc2 = nn.Linear(300, 100, bias=True)
self.r2 = nn.ReLU()
self.fc3 = nn.Linear(100, 1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EIDOSlab/pruning-validation | LeNet300 | false | 2,176 | [
"BSD-3-Clause"
] | 0 | bd8e83cf6f564def0e193a4be0f753c768fe9e75 | https://github.com/EIDOSlab/pruning-validation/tree/bd8e83cf6f564def0e193a4be0f753c768fe9e75 |
ResBlk | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def normalize(x, eps=1e-10):
return x * torch.rsqrt(torch.sum(x ** 2, dim=1, keepdim=True) + eps)
class ResBlk(nn.Module):
def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), normalize=
Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.utils.data
as... | Noodles-321/RegistrationEval | ResBlk | false | 8,651 | [
"MIT"
] | 38 | 3631d3d5bd65acf980fcfed803fa6125970f3e88 | https://github.com/Noodles-321/RegistrationEval/tree/3631d3d5bd65acf980fcfed803fa6125970f3e88 |
LinearModel | import torch
from torch import nn
class LinearModel(nn.Module):
def __init__(self, context_points: 'int'):
super().__init__()
self.window = context_points
self.linear = nn.Linear(context_points, 1)
def forward(self, y_c):
_bs, _length, d_y = y_c.shape
inp = y_c[:, -se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Piki1989/spacetimeformer | LinearModel | false | 14,206 | [
"MIT"
] | 209 | 7e0caf17dd03e5d25e2766c4f7132805779bcc40 | https://github.com/Piki1989/spacetimeformer/tree/7e0caf17dd03e5d25e2766c4f7132805779bcc40 |
ODEfunc_single_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
from torch._inductor.runtime.... | shuj1234/Hopfield-ODE | ODEfunc_single_conv | false | 10,823 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd |
FFN | import torch
import torch.nn as nn
class FFN(nn.Module):
def __init__(self, input_dim, num_class):
super().__init__()
self.layer1 = nn.Linear(input_dim, 256)
self.layer2 = nn.Linear(256, 128)
self.layer3 = nn.Linear(128, 128)
self.out = nn.Linear(128, num_class)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | baburamShapure/federatedGraphConv | FFN | false | 3,157 | [
"MIT"
] | 0 | 015e502fcf1b911ab23572b00c547591a4bdf378 | https://github.com/baburamShapure/federatedGraphConv/tree/015e502fcf1b911ab23572b00c547591a4bdf378 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""
A network with a single convolution layer. This is used for testing flop
count for convolution layers.
"""
def __init__(self, conv_dim: 'int', input_dim: 'int', output_dim: 'int',
kernel_size: 'int', spatial_dim: 'int', stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DenXX/fvcore | ConvNet | false | 2,217 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
ScaledDotProductAttention | # 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.... | quanha72/mesh-memory-transformer | ScaledDotProductAttention | false | 12,919 | [
"BSD-3-Clause"
] | 0 | 0eeae459efdb8e85926ce8595536409fdbfc4f99 | https://github.com/quanha72/mesh-memory-transformer/tree/0eeae459efdb8e85926ce8595536409fdbfc4f99 |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import functools
from torch ... | Lotayou/BasicSR | CharbonnierLoss | false | 2,576 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
MyLinear | # 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... | Shadowalker1995/Tutorial-Resource | MyLinear | false | 14,392 | [
"Apache-2.0"
] | 362 | 71fe3d521cf9971f708fa9978e9c685c0dda6ba6 | https://github.com/Shadowalker1995/Tutorial-Resource/tree/71fe3d521cf9971f708fa9978e9c685c0dda6ba6 |
EncoderBasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLPBlock(nn.Module):
def __init__(self, in_dim, mlp_dim, out_dim, dropout_rate=0.1):
super(MLPBlock, self).__init__()
self.fc1 = nn.Linear(in_dim, mlp_dim)
self.fc2 = nn.Linear(mlp_dim, out_dim)
self.Gelu = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | longxianlei/UtilsTools | EncoderBasicBlock | false | 10,510 | [
"MIT"
] | 0 | f45c648eb679ed59bb512b61a1af52938e326ac3 | https://github.com/longxianlei/UtilsTools/tree/f45c648eb679ed59bb512b61a1af52938e326ac3 |
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... | Elameri/ivadomed | MultiClassDiceLoss | false | 9,306 | [
"MIT"
] | 0 | 76b5cea46f90f938aafd5ec26e072d559c764b43 | https://github.com/Elameri/ivadomed/tree/76b5cea46f90f938aafd5ec26e072d559c764b43 |
DoubleAttention | # 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.... | rushirajsherlocked/External-Attention-pytorch | DoubleAttention | false | 4,222 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
MSELoss | # 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... | anglixjtu/MSG_CHN_WACV20 | MSELoss | false | 14,849 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
PSNRLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | gf0507033/kornia | PSNRLoss | false | 12,421 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 2624f40a62d3639e6d946f3ca41fd1ce4b9de82d | https://github.com/gf0507033/kornia/tree/2624f40a62d3639e6d946f3ca41fd1ce4b9de82d |
RecCrossEntropyLoss | import torch
from torch import nn
class RecCrossEntropyLoss(nn.Module):
def __init__(self, rec_ratio):
super(RecCrossEntropyLoss, self).__init__()
self.rec_ratio = rec_ratio
def forward(self, rec, inputs, logits, targets):
rec_loss = nn.MSELoss()
cls_loss = nn.CrossEntropyLos... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | YuhengZhi/Attention-Net-with-MNIST- | RecCrossEntropyLoss | false | 6,019 | [
"MIT"
] | 1 | aa6805e4df777dee1056d5f4f4f9a9b1e4a5e4ff | https://github.com/YuhengZhi/Attention-Net-with-MNIST-/tree/aa6805e4df777dee1056d5f4f4f9a9b1e4a5e4ff |
ScaledSiLU | # 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... | chris-price19/ocp | ScaledSiLU | false | 1,692 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
MaskedMSE | import torch
import torch.nn as nn
class MaskedMSE(nn.Module):
def __init__(self):
super(MaskedMSE, self).__init__()
self.criterion = nn.MSELoss()
def forward(self, input, target, gamma=2.0):
mask = gamma * target / (target + 1e-07)
self.loss = self.criterion(input * mask, ta... | 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... | dhruvramani/AccentTransfer | MaskedMSE | false | 1,832 | [
"MIT"
] | 0 | 63a35b4aa37bc41c1f66dfb4bae76e2924183d7c | https://github.com/dhruvramani/AccentTransfer/tree/63a35b4aa37bc41c1f66dfb4bae76e2924183d7c |
RelativeMSE | import torch
import torch as th
class RelativeMSE(th.nn.Module):
"""Relative Mean-Squared Error.
:math:`0.5 * \\frac{(x - y)^2}{y^2 + \\epsilon}`
Args:
eps(float): small number to avoid division by 0.
"""
def __init__(self, eps=0.01):
super(RelativeMSE, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | PeterZs/sbmc | RelativeMSE | false | 5,710 | [
"Apache-2.0"
] | 1 | ac3f5452efe0166ea73942f37cc60b1f0e1ee555 | https://github.com/PeterZs/sbmc/tree/ac3f5452efe0166ea73942f37cc60b1f0e1ee555 |
SqueezeEmbedding | import torch
import torch.nn as nn
class SqueezeEmbedding(nn.Module):
"""
Squeeze sequence embedding length to the longest one in the batch
"""
def __init__(self, batch_first=True):
super(SqueezeEmbedding, self).__init__()
self.batch_first = batch_first
def forward(self, x, x_len... | 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... | froth-synthesio/PyABSA | SqueezeEmbedding | false | 15,367 | [
"MIT"
] | 199 | 61406e7a49f93f6c986dfd7e583d730b69c2861c | https://github.com/froth-synthesio/PyABSA/tree/61406e7a49f93f6c986dfd7e583d730b69c2861c |
ComposeModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def forward(self, x):
if len(x.size()) > 1:
return x / x.norm(p=2, dim=1, keepdim=True)
else:
return x / x.norm(p=2)
class NonLinearModel(nn.Module):
def __init__(self, input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ycsun2017/simple_transfer | ComposeModel | false | 4,615 | [
"Apache-2.0"
] | 0 | b807f7a9d818c5586c101f616d190fe9968fabbd | https://github.com/ycsun2017/simple_transfer/tree/b807f7a9d818c5586c101f616d190fe9968fabbd |
MatchingTensor | import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchingTensor(nn.Module):
"""
Module that captures the basic interactions between two tensors.
:param matching_dims: Word dimension of two interaction texts.
:param channels: Number of word interaction tensor channels.
:par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zfjsail/MatchZoo-py | MatchingTensor | false | 4,700 | [
"Apache-2.0"
] | 0 | c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 | https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 |
Generative_Model | import torch
import torch.nn as nn
class Generative_Model(nn.Module):
def __init__(self, input_size, hidden_size_1, hidden_size_2,
output_size, n_classes):
super(Generative_Model, self).__init__()
self.input_size = input_size
self.hidden_size_1 = hidden_size_1
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
assert_... | frhrdr/MMD-GAN | Generative_Model | false | 10,069 | [
"Apache-2.0"
] | 0 | 7522093498b658026344541ddd5c248095763fb6 | https://github.com/frhrdr/MMD-GAN/tree/7522093498b658026344541ddd5c248095763fb6 |
FPNOutput | # 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... | dani3l125/TDNet | FPNOutput | false | 15,112 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RPrenger/NeMo | MultiHeadAttention | false | 5,746 | [
"Apache-2.0"
] | 1 | e8912ca6e3321347272a6a7da18e052812fb2062 | https://github.com/RPrenger/NeMo/tree/e8912ca6e3321347272a6a7da18e052812fb2062 |
Vflip | import torch
import torch.nn as nn
def vflip(input: 'torch.Tensor') ->torch.Tensor:
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input (torch.Tensor): input tensor
Returns:
t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | connorlee77/kornia | Vflip | false | 6,479 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | af5b1f76bedf2a7fc0e0da2386b1be3032b6534f | https://github.com/connorlee77/kornia/tree/af5b1f76bedf2a7fc0e0da2386b1be3032b6534f |
My_loss_focus | # 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... | H-Liu1997/Pytorch_Pose_Estimation_Framework | My_loss_focus | false | 5,245 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
SimpleXorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleXorModule(torch.nn.Module):
def __init__(self):
super(SimpleXorModule, self).__init__()
def forward(self, a, b):
c = torch.logical_xor(a, b)
return torch.logical_xor(c, c)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleXorModule | false | 14,678 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
LogitKLDivLoss | # 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 ... | ygnn123/training_extensions | LogitKLDivLoss | false | 4,689 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
MSECompositionLoss | # 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 functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | akimotty877/mmediting | MSECompositionLoss | false | 3,065 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | KUMartin77/AAA738_StyleGAN_pytorch | NoiseInjection | false | 11,597 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
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
from torch._inductor.runtime.... | Sheriff-A/CNN | CNN | false | 9,478 | [
"MIT"
] | 0 | 59fc187e7cdf92379f52c4f942424d3a5042bf3e | https://github.com/Sheriff-A/CNN/tree/59fc187e7cdf92379f52c4f942424d3a5042bf3e |
value_model | # 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.... | anthonytec2/ssp-rl-final | value_model | false | 3,125 | [
"MIT"
] | 0 | 4004678f7b820989d69824bd492307b3ed227b7a | https://github.com/anthonytec2/ssp-rl-final/tree/4004678f7b820989d69824bd492307b3ed227b7a |
ZeroLayer | # 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.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | ZeroLayer | false | 4,276 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
TracedModule | import torch
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 TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.quantization
import torch.onnx
import torch.nn.parallel
import tor... | LeeSHa00/PyTorch-tutorials-kr | TracedModule | false | 11,849 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
ScaledSiLU | import torch
class ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Irlirion/ocp | ScaledSiLU | false | 13,841 | [
"MIT",
"BSD-3-Clause"
] | 242 | 6fb3e794eef31559db990300198eca20f41d8f37 | https://github.com/Irlirion/ocp/tree/6fb3e794eef31559db990300198eca20f41d8f37 |
SqueezeExcitation | import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import functional as F
class SqueezeExcitation(nn.Module):
def __init__(self, input_c: 'int', expand_c: 'int', squeeze_factor: 'int'=4
):
super(SqueezeExcitation, self).__init__()
squeeze_c = input_c // squeeze_fact... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | NephrenCake/FlameRecognition | SqueezeExcitation | false | 9,408 | [
"MIT"
] | 0 | 3075a345b51c2c855a5cb2decd839065230e1484 | https://github.com/NephrenCake/FlameRecognition/tree/3075a345b51c2c855a5cb2decd839065230e1484 |
BCEDiceLoss | # 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... | g-freire/Brain-Tumor-Segmentation | BCEDiceLoss | false | 15,386 | [
"MIT"
] | 156 | e4f258feb64c11815570e295c58bda78afd21ab9 | https://github.com/g-freire/Brain-Tumor-Segmentation/tree/e4f258feb64c11815570e295c58bda78afd21ab9 |
CriticNet | from torch.nn import Module
import torch
from torch.nn import Linear
import torch.nn.functional as F
class CriticNet(Module):
def __init__(self, hidden_size):
super(CriticNet, self).__init__()
self.l1 = Linear(hidden_size, hidden_size // 2)
self.l2 = Linear(hidden_size // 2, 1)
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | geektoni/AlphaNPI | CriticNet | false | 3,534 | [
"MIT"
] | 0 | ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 | https://github.com/geektoni/AlphaNPI/tree/ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 |
Highway | import torch
from torch import nn
from torch.nn import functional as F
class Highway(nn.Module):
"""The Highway update layer from [srivastava2015]_.
.. [srivastava2015] Srivastava, R. K., *et al.* (2015).
`Highway Networks <http://arxiv.org/abs/1505.00387>`_.
*arXiv*, 1505.00387.
"""
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
from torch import nn
assert_s... | GavEdwards/chemicalx | Highway | false | 11,441 | [
"Apache-2.0"
] | 0 | 400a983ae6ba88ae0b632d021627dbdadd47b0d0 | https://github.com/GavEdwards/chemicalx/tree/400a983ae6ba88ae0b632d021627dbdadd47b0d0 |
FixedNorm | # 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_... | BlinkDL/RWKV-LM | FixedNorm | false | 13,402 | [
"BSD-2-Clause"
] | 102 | b48aa1d430a71ced8ae6a665c47f5dbd95f6f6ab | https://github.com/BlinkDL/RWKV-LM/tree/b48aa1d430a71ced8ae6a665c47f5dbd95f6f6ab |
Block | import torch
import torch.nn as nn
from functools import partial
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Huzhen757/Conformer | Block | false | 5,354 | [
"Apache-2.0"
] | 1 | 4f7a80cec28b9ced8c0225a85a32997f7cd2b93c | https://github.com/Huzhen757/Conformer/tree/4f7a80cec28b9ced8c0225a85a32997f7cd2b93c |
TransposeConv2dLayer | # 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 ... | LenKerr/Semantic-Colorization-GAN | TransposeConv2dLayer | false | 5,530 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
CAMMNISTClassifier | import torch
from torch import nn as nn
from torch import optim as optim
from torchvision import transforms as transforms
class CAMMNISTClassifier(nn.Module):
def __init__(self):
super(CAMMNISTClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.relu1 = nn.ReLU()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 as nn
fr... | RobinMaas95/GTSRB_Visualization | CAMMNISTClassifier | false | 1,008 | [
"MIT"
] | 0 | fa837ff94e089a936ef4f4418970d262b35f70b6 | https://github.com/RobinMaas95/GTSRB_Visualization/tree/fa837ff94e089a936ef4f4418970d262b35f70b6 |
MixerBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class MlpBlock(nn.Module):
def __init__(self, features, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.features = features
self.fc1 = nn.Linear(self.features, self.hidden_dim)
self.fc2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | amayuelas/NNKGReasoning | MixerBlock | false | 6,191 | [
"MIT"
] | 1 | 0e3623b344fd4e3088ece897f898ddbb1f80888d | https://github.com/amayuelas/NNKGReasoning/tree/0e3623b344fd4e3088ece897f898ddbb1f80888d |
AttLayer | # 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.... | MIracleyin/RecBole-notebook | AttLayer | false | 9,569 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
ConvReluPool | import torch
from torch.nn import Conv2d
from torch import nn
from torch.nn import functional as F
def Pool(k, stride=1, pad=0):
return torch.nn.MaxPool2d(k, stride=stride, padding=pad)
class ConvReluPool(nn.Module):
def __init__(self, fIn, fOut, k, stride=1, pool=2):
super().__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.nn import Conv2d
f... | smearle/neural-mmo | ConvReluPool | false | 12,994 | [
"MIT"
] | 0 | 7f1e98857cb32bdb59a273eb71ec43bbd9793b34 | https://github.com/smearle/neural-mmo/tree/7f1e98857cb32bdb59a273eb71ec43bbd9793b34 |
BinaryFocalLossWithLogits | import torch
import torch.nn as nn
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | BinaryFocalLossWithLogits | false | 282 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
LearnedPositionalEmbedding1D | # 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
import torch.nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | arkel23/yuwu | LearnedPositionalEmbedding1D | false | 6,229 | [
"MIT"
] | 1 | 4dcf0e18693e09a947569ddcc7cb3ff00c7c674a | https://github.com/arkel23/yuwu/tree/4dcf0e18693e09a947569ddcc7cb3ff00c7c674a |
ResizeTransform | import torch
import torch.nn as nn
import torch.nn.functional as nnf
import torch.utils
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 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.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Alison-brie/MultiPropReg | ResizeTransform | false | 7,633 | [
"MIT"
] | 14 | 526d843b161c0e2e53ec5c7c47de6964c6a44c60 | https://github.com/Alison-brie/MultiPropReg/tree/526d843b161c0e2e53ec5c7c47de6964c6a44c60 |
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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | BigRedT/gpv-1 | GeLU | false | 13,386 | [
"Apache-2.0"
] | 45 | 6a0c2173b44961cb492d00f94864c461aa77641d | https://github.com/BigRedT/gpv-1/tree/6a0c2173b44961cb492d00f94864c461aa77641d |
ConvMeanPool | import torch
import torch.nn as nn
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | henryaddison/score_sde_pytorch | ConvMeanPool | false | 12,503 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Liamkuo/SAIR | ModulatedConv2d | false | 17,637 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Steffen-Wolf/vit-pytorch | LayerNorm | false | 9,611 | [
"MIT"
] | 0 | 4f590b9bd570091d9070a039ad33301516caa341 | https://github.com/Steffen-Wolf/vit-pytorch/tree/4f590b9bd570091d9070a039ad33301516caa341 |
SequenceSummaryLayer | # 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 ... | Zaaachary/CSQA | SequenceSummaryLayer | false | 1,294 | [
"BSD-3-Clause"
] | 0 | 6da6e076f67e9458deacb665d31463db14c7d860 | https://github.com/Zaaachary/CSQA/tree/6da6e076f67e9458deacb665d31463db14c7d860 |
ActorNet | 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 ActorNet(nn.Module):
def __init__(self, state_size, action_size, fc1_units=128, fc2_units=128):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bwosh/DRL_ContinuousControl | ActorNet | false | 9,837 | [
"MIT"
] | 0 | 34314cd600f0da428bc6dddf1b89b64bc04d43df | https://github.com/bwosh/DRL_ContinuousControl/tree/34314cd600f0da428bc6dddf1b89b64bc04d43df |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | ArashVahabpour/encoder4editing-contrastive | ModulatedConv2d | false | 13,310 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
GeneralAttention | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class BaseAttention(nn.Module):
def __init__(self):
super().__init__()
def forward(self, *args, **kwargs):
raise NotImplementedError
class GeneralAttention(BaseAttention):
"""General Attention"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ROBINADC/BiGRU-CRF-with-Attention-for-NER | GeneralAttention | false | 8,707 | [
"MIT"
] | 27 | b9e037ebd6e1d56500ffb60c6030013982c17ded | https://github.com/ROBINADC/BiGRU-CRF-with-Attention-for-NER/tree/b9e037ebd6e1d56500ffb60c6030013982c17ded |
DownRightShiftedConv2d | # 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... | stankevich-mipt/pixiv-tags-to-image | DownRightShiftedConv2d | false | 4,382 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
AMSoftmaxLoss | # 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.... | albertvillanova/s3prl | AMSoftmaxLoss | false | 6,152 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
BalancedL1Loss | # 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... | AlphaLFC/mmdetection | BalancedL1Loss | false | 4,839 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
WeightNormLinear | # 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 ... | AntixK/Neural-Blocks | WeightNormLinear | false | 17,017 | [
"MIT"
] | 3 | 018a44bbb703fc848234b95a3e604576bd9df88f | https://github.com/AntixK/Neural-Blocks/tree/018a44bbb703fc848234b95a3e604576bd9df88f |
UpSample | import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super().__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BlairLee/dataset-insights | UpSample | false | 5,190 | [
"Apache-2.0"
] | 1 | 892e2ed3a2facf97cfa3a883700830d959a0c49b | https://github.com/BlairLee/dataset-insights/tree/892e2ed3a2facf97cfa3a883700830d959a0c49b |
BinaryFocalLoss | import torch
import torch.nn as nn
def binary_focal_loss(pred, target, gamma=2.0, alpha=-1, reduction='mean'):
p = torch.sigmoid(pred)
loss_pos = -target * (1.0 - p) ** gamma * torch.log(p + 1e-09)
loss_neg = -(1.0 - target) * p ** gamma * torch.log(1.0 - p + 1e-09)
if alpha >= 0.0 and alpha <= 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
import torch.nn as nn
... | VisualComputingInstitute/Person_MinkUNet | BinaryFocalLoss | false | 18,041 | [
"MIT"
] | 4 | fa39764245a022740c0a3d8c85026532fff93e74 | https://github.com/VisualComputingInstitute/Person_MinkUNet/tree/fa39764245a022740c0a3d8c85026532fff93e74 |
QNetwork | # 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_... | AntoniaSophia/deep-reinforcement-learning | QNetwork | false | 8,859 | [
"MIT"
] | 0 | 1d1c77039eea22fcf6726c35c3dd2563adfcb519 | https://github.com/AntoniaSophia/deep-reinforcement-learning/tree/1d1c77039eea22fcf6726c35c3dd2563adfcb519 |
SimpleCumSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCumSumModule(torch.nn.Module):
def __init__(self, dim):
super(SimpleCumSumModule, self).__init__()
self.dim = dim
def forward(self, tensor):
return torch.cumsum(tensor, self.dim)
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
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleCumSumModule | false | 14,663 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
AsymmetricLoss | import torch
import torch.nn as nn
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super(AsymmetricLoss, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
self.cl... | 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... | Pepijnnn/MasterThesis | AsymmetricLoss | false | 933 | [
"MIT"
] | 0 | 7ec831f5e55f5f181e0196fa78284e2846ce2e26 | https://github.com/Pepijnnn/MasterThesis/tree/7ec831f5e55f5f181e0196fa78284e2846ce2e26 |
Linear | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn.modules.loss
class Linear(Module):
"""
to embedding feature
"""
def __init__(self, in_features, out_features, dropout=0.0, act=F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | goodman1204/CAN-pytorch | Linear | false | 12,462 | [
"MIT"
] | 0 | 73d9486c93dd069101c750f94a0750fff0500abb | https://github.com/goodman1204/CAN-pytorch/tree/73d9486c93dd069101c750f94a0750fff0500abb |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim
import torch.autograd
class Policy(nn.Module):
def __init__(self, learning_rate, gamma, in_dim, out_dim):
super(Policy, self).__init__()
self.learning_rate = learning_rate
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.... | ChangQingAAS/Deep-Reinforcement-Learning | Policy | false | 591 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
Recon_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | albangossard/Course-inverse-problems-and-unrolled-networks | Recon_Block | false | 1,399 | [
"MIT"
] | 0 | 0d4161c905149817e3abff9e70c101f36fac4270 | https://github.com/albangossard/Course-inverse-problems-and-unrolled-networks/tree/0d4161c905149817e3abff9e70c101f36fac4270 |
CORblock_Z | # 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 ... | ViCCo-Group/THINGSvision | CORblock_Z | false | 14,561 | [
"MIT"
] | 45 | 27273564631605639287f9b3bd3c57ba8cdb720f | https://github.com/ViCCo-Group/THINGSvision/tree/27273564631605639287f9b3bd3c57ba8cdb720f |
Net1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AndreaCeccarelli/gpu-monitor | Net1 | false | 16,916 | [
"MIT"
] | 4 | aad4dc88387a69235e9c370cb08da1f16ba4aa96 | https://github.com/AndreaCeccarelli/gpu-monitor/tree/aad4dc88387a69235e9c370cb08da1f16ba4aa96 |
Accuracy | import torch
from torch import nn
def accuracy(logits, labels, ignore_index: 'int'=-100):
with torch.no_grad():
valid_mask = labels != ignore_index
predictions = logits.float().argmax(-1)
correct = (predictions == labels) * valid_mask
return correct.sum().float() / valid_mask.sum()... | 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... | MachineLearningLifeScience/What-is-a-meaningful-representation-of-protein-sequences | Accuracy | false | 17,667 | [
"BSD-3-Clause"
] | 4 | 2c24db6ee8763b0b6098d7509cf3325647931c11 | https://github.com/MachineLearningLifeScience/What-is-a-meaningful-representation-of-protein-sequences/tree/2c24db6ee8763b0b6098d7509cf3325647931c11 |
PointLoss | import torch
import torch.nn.parallel
import torch.utils.data
import torch.nn as nn
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... | 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.parallel
import torch.utils.data
import torch.nn as nn
assert_size_stride... | liuyuex97/PF-Net-Point-Fractal-Network | PointLoss | false | 12,725 | [
"MIT"
] | 0 | 97f248a03bcd33828e8e2175ec79bbe8c791952d | https://github.com/liuyuex97/PF-Net-Point-Fractal-Network/tree/97f248a03bcd33828e8e2175ec79bbe8c791952d |
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_... | YNNEKUW/captum | BasicModel_ConvNet_One_Conv | false | 12,001 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
NormalizationLayer | # 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | akurniawan/jina-hub | NormalizationLayer | false | 1,395 | [
"Apache-2.0"
] | 0 | d89bc5e8f527f1212c3228a15775e222983c0087 | https://github.com/akurniawan/jina-hub/tree/d89bc5e8f527f1212c3228a15775e222983c0087 |
IMul | import torch
class IMul(torch.nn.Module):
def __init__(self):
super(IMul, self).__init__()
def forward(self, x, y):
x *= y
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | bunderhi/torch2trt | IMul | false | 1,599 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
ExponentialDecay | import torch
from torch import Tensor
from torch import nn
from torch.jit import Final
class ExponentialUpdate(nn.Module):
alpha: 'Final[int]'
def __init__(self, alpha: 'float'):
super().__init__()
self.alpha = float(alpha)
def forward(self, x: 'Tensor', state: '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 Tensor
from torch import nn
from torch.jit import Final
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | Rikorose/clc-dns-challenge-2020 | ExponentialDecay | false | 8,705 | [
"Apache-2.0"
] | 12 | 4f1c078691327a75b3a338fe372ba356b450a6da | https://github.com/Rikorose/clc-dns-challenge-2020/tree/4f1c078691327a75b3a338fe372ba356b450a6da |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | ChristophReich1996/3D_Baggage_Segmentation | FocalLoss | false | 5,002 | [
"MIT"
] | 1 | 00392cb0fde22d3180b6baf81e404d0fcf4e2ebf | https://github.com/ChristophReich1996/3D_Baggage_Segmentation/tree/00392cb0fde22d3180b6baf81e404d0fcf4e2ebf |
pdice_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
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | manuel-rdz/SGL-Retinal-Vessel-Segmentation | pdice_loss | false | 15,999 | [
"MIT"
] | 45 | 7897d977e77aa0b5d3acb86e0aa74c6829d67415 | https://github.com/manuel-rdz/SGL-Retinal-Vessel-Segmentation/tree/7897d977e77aa0b5d3acb86e0aa74c6829d67415 |
ConvNet64 | import torch
import torch.nn as nn
def get_activation(s_act):
if s_act == 'relu':
return nn.ReLU(inplace=True)
elif s_act == 'sigmoid':
return nn.Sigmoid()
elif s_act == 'softplus':
return nn.Softplus()
elif s_act == 'linear':
return None
elif s_act == 'tanh':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | GloryyrolG/normalized-autoencoders | ConvNet64 | false | 554 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
DuelingMLP | # 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_... | AlexHermansson/hindsight-experience-replay | DuelingMLP | false | 16,876 | [
"MIT"
] | 5 | 65468d523bb803123d7aab9bb83abc7a3d5db3c8 | https://github.com/AlexHermansson/hindsight-experience-replay/tree/65468d523bb803123d7aab9bb83abc7a3d5db3c8 |
PlanarNormalizingFlow | import torch
import torch.nn.functional as F
from torch import nn
class PlanarNormalizingFlow(nn.Module):
"""
Planar normalizing flow [Rezende & Mohamed 2015].
Provides a tighter bound on the ELBO by giving more expressive
power to the approximate distribution, such as by introducing
covariance be... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | chunglabmit/phathom | PlanarNormalizingFlow | false | 6,457 | [
"MIT"
] | 1 | 304db7a95e898e9b03d6b2640172752d21a7e3ed | https://github.com/chunglabmit/phathom/tree/304db7a95e898e9b03d6b2640172752d21a7e3ed |
BehlerAngular | import torch
from torch import nn as nn
class BehlerAngular(nn.Module):
"""
Compute Behler type angular contribution of the angle spanned by three atoms:
:math:`2^{(1-\\zeta)} (1 + \\lambda \\cos( {\\theta}_{ijk} ) )^\\zeta`
Sets of zetas with lambdas of -1 and +1 are generated automatically.
A... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | Avinashpathapati/gnn | BehlerAngular | false | 8,832 | [
"MIT"
] | 0 | e06c36f5d8fb7da555c8f82e04364ba4366444c7 | https://github.com/Avinashpathapati/gnn/tree/e06c36f5d8fb7da555c8f82e04364ba4366444c7 |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_ti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ononoki-Yotsugi/IDGL | GAT | false | 9,502 | [
"Apache-2.0"
] | 0 | a99f840681a4ae26c2740ed9e9302d4e15a68c7f | https://github.com/Ononoki-Yotsugi/IDGL/tree/a99f840681a4ae26c2740ed9e9302d4e15a68c7f |
RMSELoss | import torch
import torch.nn as nn
class RMSELoss(nn.Module):
def __init__(self):
super(RMSELoss, self).__init__()
self.mse = nn.MSELoss()
def forward(self, yhat, y):
return torch.sqrt(self.mse(yhat, y))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | PARMAGroup/UNet-Instance-Cell-Segmentation | RMSELoss | false | 8,626 | [
"MIT"
] | 30 | 79655a2c5781d2e20c7d5760f631fbb0be392292 | https://github.com/PARMAGroup/UNet-Instance-Cell-Segmentation/tree/79655a2c5781d2e20c7d5760f631fbb0be392292 |
SoftQNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003):
super(SoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
self.linear2 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | biemann/rl-testbed-for-energyplus | SoftQNetwork | false | 9,802 | [
"MIT"
] | 0 | a01be4d12eda970b352729ff6cb4a3eea8ddee6a | https://github.com/biemann/rl-testbed-for-energyplus/tree/a01be4d12eda970b352729ff6cb4a3eea8ddee6a |
SelfAttentionGated | # 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.... | xdong73S/Match_LSTM_v2.0 | SelfAttentionGated | false | 4,643 | [
"MIT"
] | 0 | dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 | https://github.com/xdong73S/Match_LSTM_v2.0/tree/dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 |
GaussianFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | alsgkals2/SRResCGAN | GaussianFilter | false | 14,830 | [
"MIT"
] | 81 | a71201a93e1819045f9c7711743812546d3a1f31 | https://github.com/alsgkals2/SRResCGAN/tree/a71201a93e1819045f9c7711743812546d3a1f31 |
ReLU | # 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, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | DoggyLiu0116/MamboNet | ReLU | false | 5,081 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
resBlock | # 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.... | SeokjaeLIM/DSLR-release | resBlock | false | 8,744 | [
"Apache-2.0"
] | 14 | 861429482faf50ee3d6570948af8c48df1fc7f43 | https://github.com/SeokjaeLIM/DSLR-release/tree/861429482faf50ee3d6570948af8c48df1fc7f43 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.