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 |
|---|---|---|---|---|---|---|---|---|---|---|
LayerNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LayerNorm(nn.Module):
def __init__(self, eps=0.0001):
super(LayerNorm, self).__init__()
self.eps = eps
def forward(self, x):
x_shape = x.sh... | 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 |
NeuralAccumulatorCell | # 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 ... | mikomel/machine-number-sense | NeuralAccumulatorCell | false | 7,228 | [
"MIT"
] | 1 | 173b67e4f25bd8249ba4a41904d4cd4af26bae05 | https://github.com/mikomel/machine-number-sense/tree/173b67e4f25bd8249ba4a41904d4cd4af26bae05 |
ISub | import torch
class ISub(torch.nn.Module):
def __init__(self):
super(ISub, 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_sub_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | NVIDIA-AI-IOT-private/torch2trt | ISub | false | 10,516 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | Exir-lxr/crldr-prune-pytorch | SEModule | false | 2,305 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
RegModel | import torch
import torch.nn as nn
from typing import *
class RegModel(nn.Module):
def __init__(self):
super().__init__()
self.a, self.b = nn.Parameter(torch.randn(1)), nn.Parameter(torch.
randn(1))
def forward(self, x):
return x * self.a + self.b
def get_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
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DineshChauhan/fastai_docs | RegModel | false | 11,345 | [
"Apache-2.0"
] | 0 | cf4d88073fb6f3ef7331b5360618b8dd95eb9345 | https://github.com/DineshChauhan/fastai_docs/tree/cf4d88073fb6f3ef7331b5360618b8dd95eb9345 |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | f4str/digit-recognizer | FeedForward | false | 3,490 | [
"MIT"
] | 0 | 67c175c683b22a3bf9d8a28dce812a82e08039d5 | https://github.com/f4str/digit-recognizer/tree/67c175c683b22a3bf9d8a28dce812a82e08039d5 |
LayerNorm | import torch
class LayerNorm(torch.nn.Module):
def __init__(self, nout: 'int'):
super(LayerNorm, self).__init__()
self.layer_norm = torch.nn.LayerNorm(nout, eps=1e-12)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = self.layer_norm(x.transpose(1, -1))
x = x.transpose... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | karan-deepsync/FastSpeech2 | LayerNorm | false | 15,783 | [
"Apache-2.0"
] | 148 | 84ad261db4a865536b2e15dfb8346644c3192704 | https://github.com/karan-deepsync/FastSpeech2/tree/84ad261db4a865536b2e15dfb8346644c3192704 |
MultiHeadAttn | # 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.... | JasonBenn/duet | MultiHeadAttn | false | 8,353 | [
"Apache-2.0"
] | 11 | 0d6f1f66fad097023b022f2a361a1587d0f740ba | https://github.com/JasonBenn/duet/tree/0d6f1f66fad097023b022f2a361a1587d0f740ba |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MikoyChinese/Learn | DiceLoss | false | 844 | [
"Apache-2.0"
] | 0 | c482b1e84496279935b5bb2cfc1e6d78e2868c63 | https://github.com/MikoyChinese/Learn/tree/c482b1e84496279935b5bb2cfc1e6d78e2868c63 |
CoFusion | # 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.... | jechague/DexiNed | CoFusion | false | 15,687 | [
"MIT"
] | 471 | 370fe9031579b2d815ab706d7dc9daf23b969a87 | https://github.com/jechague/DexiNed/tree/370fe9031579b2d815ab706d7dc9daf23b969a87 |
EnDown | import torch
import torch.nn as nn
import torch.optim
class EnDown(nn.Module):
def __init__(self, in_channels, out_channels):
super(EnDown, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
stride=2, padding=1)
def forward(self, x):
y = 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
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | felixquinton1/TransBTS | EnDown | false | 10,169 | [
"Apache-2.0"
] | 0 | 6992c902413ba15f40ebfe9f6d5d0e3594051033 | https://github.com/felixquinton1/TransBTS/tree/6992c902413ba15f40ebfe9f6d5d0e3594051033 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | ojasjoshi/Selective_Deblur_GANs | Conv2d | false | 16,202 | [
"MIT"
] | 1,663 | 9ac256b41b62c50c8b967f7e6fa7ecb4c7305889 | https://github.com/ojasjoshi/Selective_Deblur_GANs/tree/9ac256b41b62c50c8b967f7e6fa7ecb4c7305889 |
squeeze | import torch
import torch.nn as nn
class squeeze(nn.Module):
def __init__(self, block_size):
super(squeeze, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def inverse(self, input):
output = input.permute(0, 2, 3, 1)
batch_si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Schwartz-Zha/My-invertible-resnet | squeeze | false | 1,027 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
LinearWithGroupNorm | # 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.... | motional/nuplan-devkit | LinearWithGroupNorm | false | 16,121 | [
"Apache-2.0"
] | 128 | e39029e788b17f47f2fcadb774098ef8fbdd0d67 | https://github.com/motional/nuplan-devkit/tree/e39029e788b17f47f2fcadb774098ef8fbdd0d67 |
my_BinaryCross | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class my_BinaryCross(nn.Module):
def __init__(self, args):
super(my_BinaryCross, self).__init__()
self.args = args
def forward(self, output, target, beat):
modif_beat = 1.0 / torch.exp(beat) * 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | carsault/chord_sequence_prediction | my_BinaryCross | false | 1,679 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 |
SineLayer | import math
import torch
import torch.nn as nn
class SineLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True, is_first=False,
omega_0=30):
super().__init__()
self.omega_0 = omega_0
self.is_first = is_first
self.in_features = in_features
self.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
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
i... | etienne87/pytorch-cifar | SineLayer | false | 10,089 | [
"MIT"
] | 0 | d9164df8ba0cb9259daf857e006db3fecb762af7 | https://github.com/etienne87/pytorch-cifar/tree/d9164df8ba0cb9259daf857e006db3fecb762af7 |
CausalConv1d | # 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... | Hao-Kailong/DisFeb | CausalConv1d | false | 518 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
DownsampleA | # 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... | YasufumiSakai/Pruning | DownsampleA | false | 6,007 | [
"BSD-3-Clause"
] | 1 | 5c8bc0d780fab41e1bd894b0360bd50e14cd0571 | https://github.com/YasufumiSakai/Pruning/tree/5c8bc0d780fab41e1bd894b0360bd50e14cd0571 |
SimpleReciprocalModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReciprocalModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReciprocalModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = tensor + tensor
return othe... | 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 | SimpleReciprocalModel | false | 14,679 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Block | import torch
import torch.nn as nn
class Block(nn.Module):
"""
A ResNet module.
"""
def __init__(self, iDim, hDim):
super().__init__()
self.W0 = nn.Linear(iDim, hDim)
self.W1 = nn.Linear(hDim, iDim)
def LS(w):
return w.weight.numel() + w.bias.numel()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | sparseinference/argmaxnet | Block | false | 10,800 | [
"MIT"
] | 0 | ff1e090a662d384f2ba4349494c9630079d2545b | https://github.com/sparseinference/argmaxnet/tree/ff1e090a662d384f2ba4349494c9630079d2545b |
GeometricLoss | import torch
import numpy as np
import torch.nn as nn
class GeometricLoss(nn.Module):
def __init__(self, num_parameters=2, init=[0.0, -3.0]):
self.num_parameters = num_parameters
super(GeometricLoss, self).__init__()
assert len(init) == num_parameters
self.weight = nn.Parameter(to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | sanfengliao/DeepNavi | GeometricLoss | false | 10,732 | [
"Apache-2.0"
] | 0 | dc405ac0010075c2eea63083528db7cb765ad161 | https://github.com/sanfengliao/DeepNavi/tree/dc405ac0010075c2eea63083528db7cb765ad161 |
LayerNormalization | import torch
from torch import nn
from torch.autograd import *
class LayerNormalization(nn.Module):
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.beta = nn.Parameter(torch.zeros(d_hid)... | 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
from torch.autograd import *
assert_size_stride = torch._C... | learnerhouse/ner-bert | LayerNormalization | false | 15,873 | [
"MIT"
] | 391 | 606328a27a7313b6c22b78590e06618ad77402cd | https://github.com/learnerhouse/ner-bert/tree/606328a27a7313b6c22b78590e06618ad77402cd |
BCEAfterSigmoidLoss | import torch
from torch import nn
from torch.nn import functional
import torch.autograd
class Loss(nn.Module):
"""A loss function."""
class PointwiseLoss(Loss):
"""Pointwise loss functions compute an independent loss term for each triple-label pair."""
class BCEAfterSigmoidLoss(PointwiseLoss):
"""A lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Sina-Baharlou/pykeen | BCEAfterSigmoidLoss | false | 11,881 | [
"MIT"
] | 0 | 89984e0f7a490f3c0f0d936564b7744097130d15 | https://github.com/Sina-Baharlou/pykeen/tree/89984e0f7a490f3c0f0d936564b7744097130d15 |
WeightNormTransConv2d | # 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 | WeightNormTransConv2d | false | 17,031 | [
"MIT"
] | 3 | 018a44bbb703fc848234b95a3e604576bd9df88f | https://github.com/AntixK/Neural-Blocks/tree/018a44bbb703fc848234b95a3e604576bd9df88f |
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
import torc... | xkp793003821/kaggle-tgs-salt | FocalLoss | false | 4,584 | [
"MIT"
] | 0 | 4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 | https://github.com/xkp793003821/kaggle-tgs-salt/tree/4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 |
LinearEmbedding | import math
import torch
import torch.utils.data
import torch.nn as nn
class LinearEmbedding(nn.Module):
def __init__(self, inp_size, d_model):
super(LinearEmbedding, self).__init__()
self.lut = nn.Linear(inp_size, d_model)
self.d_model = d_model
def forward(self, x):
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Akhil-Raj/Trajectory-Transformer | LinearEmbedding | false | 1 | [
"MIT"
] | 0 | dd09fda99443f6afb59d962026573162219ea6a9 | https://github.com/Akhil-Raj/Trajectory-Transformer/tree/dd09fda99443f6afb59d962026573162219ea6a9 |
maxout | import torch
import torch.nn as nn
import torch.utils.data
class maxout(nn.Module):
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 = pool_size
self.linear = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | xuehuiping/Global-Encoding | maxout | false | 13,116 | [
"MIT"
] | 0 | 1cba2746162ac569b430aa1ba5bca58183416ee7 | https://github.com/xuehuiping/Global-Encoding/tree/1cba2746162ac569b430aa1ba5bca58183416ee7 |
AffineGridGen | # 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.nn import Module
import torch.nn
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_s... | JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching | AffineGridGen | false | 5,412 | [
"MIT"
] | 1 | b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 | https://github.com/JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/tree/b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 |
CNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(CNet, self).__init__()
self.fcs = nn.Linear(s_dim, 30)
self.fcs.weight.data.normal_(0, 0.1)
self.fca = nn.Linear(a_dim, 30)
self.fca.weight.dat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Kernels-K/DDPG-pytorch- | CNet | false | 8,412 | [
"MIT"
] | 26 | 9a80a56f52f2232e5bd197521d3d2d388b48c882 | https://github.com/Kernels-K/DDPG-pytorch-/tree/9a80a56f52f2232e5bd197521d3d2d388b48c882 |
PixelwiseNormalization | import torch
import torch.nn as nn
class PixelwiseNormalization(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
factor = ((x ** 2).mean(dim=1, keepdim=True) + 1e-08) ** 0.5
return x / factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DannyDannyDanny/DeepPrivacy | PixelwiseNormalization | false | 2,116 | [
"MIT"
] | 0 | 749e260bdcc28a0c12d526f24e4f5315d1b447ad | https://github.com/DannyDannyDanny/DeepPrivacy/tree/749e260bdcc28a0c12d526f24e4f5315d1b447ad |
GluMlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.collect_env
assert_size_stride = torch.... | HaotianUpenn/scatterbrain | GluMlp | false | 13,748 | [
"Apache-2.0"
] | 49 | c026128d7362ae627641d11d4e5627bc1f400eb1 | https://github.com/HaotianUpenn/scatterbrain/tree/c026128d7362ae627641d11d4e5627bc1f400eb1 |
Swish | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | grofit/traiNNer | Swish | false | 15,477 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
DemodulatedConv2d | import torch
import torch.utils.data
import torch
from torchvision.transforms import functional as F
import torch.nn as nn
from torch.nn import functional as F
class DemodulatedConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1,
padding=0, bias=False, dilation=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.triton_helpers import libdevice
import torch.utils.... | seawee1/ForkGAN-pytorch | DemodulatedConv2d | false | 4,293 | [
"BSD-3-Clause"
] | 0 | 02d721875d47e4a1e96a14cc4770edcb6b68a5d0 | https://github.com/seawee1/ForkGAN-pytorch/tree/02d721875d47e4a1e96a14cc4770edcb6b68a5d0 |
CrossNet | import torch
import torch.nn as nn
from sklearn.metrics import *
class CrossNet(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)``.
Output shape
- 2D tens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 sklearn.metrics import *
assert_size_stride = torch._... | Fanxingye/DeepRS | CrossNet | false | 14,035 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d |
Highway | # 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 ... | KIONLEE/cs224n | Highway | false | 2,434 | [
"MIT"
] | 0 | 63054e187fb40d65af058673fe7aa2f22433da6e | https://github.com/KIONLEE/cs224n/tree/63054e187fb40d65af058673fe7aa2f22433da6e |
IdentityPadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class IdentityPadding(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super(IdentityPadding, self).__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
... | 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... | dnddnjs/pytorch-vision | IdentityPadding | false | 15,181 | [
"MIT"
] | 48 | d432b467774f838bef37372d6cff3576c6559803 | https://github.com/dnddnjs/pytorch-vision/tree/d432b467774f838bef37372d6cff3576c6559803 |
RegLoss | # 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_... | dreaming-qin/RecBole | RegLoss | false | 12,316 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
FFN | # 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.... | Tarandro/MOTR | FFN | false | 14,558 | [
"MIT"
] | 191 | f2bcc2df0b3bd959208e78c54a3e9d8a3434f9f4 | https://github.com/Tarandro/MOTR/tree/f2bcc2df0b3bd959208e78c54a3e9d8a3434f9f4 |
BertSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AlanFokCo/bert-chinese-horovod-elastic | BertSelfAttention | false | 7,621 | [
"Apache-2.0"
] | 1 | 02317d0857e0e8e313dd63ead61ca9996b25548e | https://github.com/AlanFokCo/bert-chinese-horovod-elastic/tree/02317d0857e0e8e313dd63ead61ca9996b25548e |
ConditionalBatchNorm2d | # 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 ... | AnonymousGFR/wbgan.pytorch | ConditionalBatchNorm2d | false | 4,879 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 |
SelfAttention | # 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.... | Zaaachary/CSQA | SelfAttention | false | 1,905 | [
"BSD-3-Clause"
] | 0 | 6da6e076f67e9458deacb665d31463db14c7d860 | https://github.com/Zaaachary/CSQA/tree/6da6e076f67e9458deacb665d31463db14c7d860 |
MSECompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | hejm37/mmediting | MSECompositionLoss | false | 12,502 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
feedforward | import math
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class feedforward(nn.Module):
def __init__(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 math
import ... | Divyanshu23/model-zoo | feedforward | false | 8,091 | [
"MIT"
] | 43 | 2eea6df691d302e182bb1ff8ec5af3542de562ba | https://github.com/Divyanshu23/model-zoo/tree/2eea6df691d302e182bb1ff8ec5af3542de562ba |
Net | # 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
... | gganssle/mixture-density-networks | Net | false | 10,109 | [
"Apache-2.0"
] | 0 | 246f05d8a1dedd259232760a1b54ac5845c4b8f6 | https://github.com/gganssle/mixture-density-networks/tree/246f05d8a1dedd259232760a1b54ac5845c4b8f6 |
DuRB_p | # 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.... | vis-opt-group/GTANet | DuRB_p | false | 4,500 | [
"MIT"
] | 0 | 269ff4418ee5f0267987e1fa4c69bda13e5cb00d | https://github.com/vis-opt-group/GTANet/tree/269ff4418ee5f0267987e1fa4c69bda13e5cb00d |
ConvModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvModel(nn.Module):
def __init__(self):
super(ConvModel, 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 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | chetanseth/pytorch | ConvModel | false | 9,906 | [
"MIT"
] | 0 | 001aaf56ee72e0a8b4df5fe8ad84fda6354a084c | https://github.com/chetanseth/pytorch/tree/001aaf56ee72e0a8b4df5fe8ad84fda6354a084c |
CoreNetwork | # 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_... | SmirnovKol/recurrent-visual-attention | CoreNetwork | false | 14,432 | [
"MIT"
] | 463 | 4cb8d9e768ae35f38439278bb8a7b4d6b253a537 | https://github.com/SmirnovKol/recurrent-visual-attention/tree/4cb8d9e768ae35f38439278bb8a7b4d6b253a537 |
Out | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | nazarblch/style-based-gan-pytorch | Out | false | 4,049 | [
"MIT"
] | 0 | 5ed7fa114904501d77b414921cd9f439773ba24c | https://github.com/nazarblch/style-based-gan-pytorch/tree/5ed7fa114904501d77b414921cd9f439773ba24c |
ChannelAttention | # 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_... | lee-zq/VesselSeg-pytorch | ChannelAttention | false | 15,897 | [
"Apache-2.0"
] | 83 | b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa | https://github.com/lee-zq/VesselSeg-pytorch/tree/b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa |
PONO | import torch
import torch.nn as nn
def pono(x, epsilon=1e-05):
"""Positional normalization"""
mean = x.mean(dim=1, keepdim=True)
std = x.var(dim=1, keepdim=True).add(epsilon).sqrt()
output = (x - mean) / std
return output, mean, std
class PONO(nn.Module):
def forward(self, x, mask=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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Warvito/lmconv | PONO | false | 14,560 | [
"MIT"
] | 69 | 01adba51e3fff1e7da99324dc64a9fc9cd38621e | https://github.com/Warvito/lmconv/tree/01adba51e3fff1e7da99324dc64a9fc9cd38621e |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | krishsethi19/dffml | ConvNet | false | 10,368 | [
"MIT"
] | 0 | 2dd0a9c4a125a9739d27228128bbd381a8e0fef4 | https://github.com/krishsethi19/dffml/tree/2dd0a9c4a125a9739d27228128bbd381a8e0fef4 |
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... | AnimeshKoratana/blurryface | MyLinear | false | 601 | [
"Apache-2.0"
] | 0 | c6cb5feec02f6d5af3acb1678336800390715d65 | https://github.com/AnimeshKoratana/blurryface/tree/c6cb5feec02f6d5af3acb1678336800390715d65 |
ReLUDeepLiftModel | import torch
import torch.nn as nn
class ReLUDeepLiftModel(nn.Module):
"""
https://www.youtube.com/watch?v=f_iAM0NPwnM
"""
def __init__(self) ->None:
super().__init__()
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
def forward(self, x1, x2, x3=2):
return 2 * self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | ReLUDeepLiftModel | false | 5,466 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
PKT | import torch
from torch import nn
class PKT(nn.Module):
"""Probabilistic Knowledge Transfer for deep representation learning
Code from author: https://github.com/passalis/probabilistic_kt"""
def __init__(self):
super(PKT, self).__init__()
def forward(self, f_s, f_t):
return self.cosi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fr... | Alibaba-MIIL/HeadSharingKD | PKT | false | 7,674 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
TVLoss | import torch
import torch.nn as nn
import torch.nn.parallel
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Blatts01/VckImageRestoration | TVLoss | false | 2,022 | [
"MIT"
] | 0 | ae4e2221d9d4e236a08722cb92ac5cc88947e311 | https://github.com/Blatts01/VckImageRestoration/tree/ae4e2221d9d4e236a08722cb92ac5cc88947e311 |
IntrinsicsModel | import torch
import torch.nn.functional as F
import torch.nn as nn
class IntrinsicsModel(nn.Module):
def __init__(self, n, H, W):
super(IntrinsicsModel, self).__init__()
self.skew_scale = 0.001
self.fc1 = nn.Linear(n, n)
self.fc2 = nn.Linear(n, n)
self.fc3 = nn.Linear(n, 5... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | apurvtwr/Jarvis | IntrinsicsModel | false | 3,126 | [
"Apache-2.0"
] | 0 | bdd25e059826a0403c6282a1ee206f9f4c3e9355 | https://github.com/apurvtwr/Jarvis/tree/bdd25e059826a0403c6282a1ee206f9f4c3e9355 |
RelPositionMultiHeadedAttention | # 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.... | Honghe/wenet | RelPositionMultiHeadedAttention | false | 5,332 | [
"Apache-2.0"
] | 1 | 4421790bec3778df591816d69f0449930a9be321 | https://github.com/Honghe/wenet/tree/4421790bec3778df591816d69f0449930a9be321 |
Glu | # 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... | debasish-mihup/EfficientConformer | Glu | false | 10,335 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
GE2ELoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def calc_loss(sim_matrix):
same_idx = list(range(sim_matrix.size(0)))
pos = sim_matrix[same_idx, :, same_idx]
neg = (torch.exp(sim_matrix).sum(dim=2) + 1e-06).log_()
per_embedding_loss = -1 * (pos - neg)
loss = per_embedding_loss.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | helia95/SpeakerRecognition_tutorial | GE2ELoss | false | 12,513 | [
"MIT"
] | 0 | 5c00f9165fd260d50b74ab46e4d81d7cfd77ab8c | https://github.com/helia95/SpeakerRecognition_tutorial/tree/5c00f9165fd260d50b74ab46e4d81d7cfd77ab8c |
ConvLayer | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | Chandan-h-509/ignite | ConvLayer | false | 8,972 | [
"BSD-3-Clause"
] | 0 | f8c39828cb1dac49b6ef358cdf77865bf2430106 | https://github.com/Chandan-h-509/ignite/tree/f8c39828cb1dac49b6ef358cdf77865bf2430106 |
MLP | import torch
import torch.nn as nn
class FC(nn.Module):
def __init__(self, in_size, out_size, dropout_r=0.0, use_relu=True):
super(FC, self).__init__()
self.dropout_r = dropout_r
self.use_relu = use_relu
self.linear = nn.Linear(in_size, out_size)
if use_relu:
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_... | JayZhu0104/openvqa | MLP | false | 2,403 | [
"Apache-2.0"
] | 0 | cc2a92dccb08fb87506d5d0dede7dcfa3a1997aa | https://github.com/JayZhu0104/openvqa/tree/cc2a92dccb08fb87506d5d0dede7dcfa3a1997aa |
Squareplus | # 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_... | MaximeRobeyns/BDRL | Squareplus | false | 838 | [
"Apache-2.0"
] | 0 | 55e295d5aaca6745d35525114b472ad118c14a6d | https://github.com/MaximeRobeyns/BDRL/tree/55e295d5aaca6745d35525114b472ad118c14a6d |
Conv2dZeros | # 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.... | KyleDavisSA/pde-surrogate | Conv2dZeros | false | 13,970 | [
"MIT"
] | 62 | 41ad2c9eb73c323e389174080f4b3df6cbd3c900 | https://github.com/KyleDavisSA/pde-surrogate/tree/41ad2c9eb73c323e389174080f4b3df6cbd3c900 |
AttentionCollapse | # 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.... | vishalbelsare/deepdow | AttentionCollapse | false | 16,704 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
GraphConvolution | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
import torch.nn.modules.loss
import torch.utils.data
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | WanyuGroup/CVPR2022-OrphicX | GraphConvolution | false | 1,206 | [
"MIT"
] | 0 | 98d8d8259439c45661573e575cf956331df16abc | https://github.com/WanyuGroup/CVPR2022-OrphicX/tree/98d8d8259439c45661573e575cf956331df16abc |
ConvBlock | # 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_... | coasxu/FedMA | ConvBlock | false | 15,053 | [
"MIT"
] | 254 | 21f4d32338fd2563ebd97c737e3b9f4f470029d9 | https://github.com/coasxu/FedMA/tree/21f4d32338fd2563ebd97c737e3b9f4f470029d9 |
CAMMNISTExtendedClassifier | # 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 as nn
fr... | RobinMaas95/GTSRB_Visualization | CAMMNISTExtendedClassifier | false | 1,017 | [
"MIT"
] | 0 | fa837ff94e089a936ef4f4418970d262b35f70b6 | https://github.com/RobinMaas95/GTSRB_Visualization/tree/fa837ff94e089a936ef4f4418970d262b35f70b6 |
BasicModel2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YNNEKUW/captum | BasicModel2 | false | 11,990 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
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
from torch._inductor.runtime.... | Chandan-h-509/ignite | Policy | false | 8,976 | [
"BSD-3-Clause"
] | 0 | f8c39828cb1dac49b6ef358cdf77865bf2430106 | https://github.com/Chandan-h-509/ignite/tree/f8c39828cb1dac49b6ef358cdf77865bf2430106 |
GCNLayer | import torch
import torch.nn as nn
class GCNLayer(nn.Module):
def __init__(self, c_in, c_out):
super().__init__()
self.projection = nn.Linear(c_in, c_out)
def forward(self, nodes_feats, adj_matrix):
num_neighbors = adj_matrix.sum(dim=-1, keepdims=True)
node_feats = self.proje... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | dogeplusplus/sandbox | GCNLayer | false | 1,856 | [
"MIT"
] | 0 | c9041c06da9454f6c3cec622abbbf918c9f13bdc | https://github.com/dogeplusplus/sandbox/tree/c9041c06da9454f6c3cec622abbbf918c9f13bdc |
BCELoss2d | import torch
import torch.nn.functional as F
import torch.nn as nn
class BCELoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
"""
Imlements Binary Cross Entropy loss function.
"""
super(BCELoss2d, self).__init__()
self.bce_loss = nn.BCELoss(weight,... | 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... | ekalyashov/segmentation-unet | BCELoss2d | false | 12,337 | [
"MIT"
] | 0 | 59dc95419481b2535a52332e0be92b15c7450674 | https://github.com/ekalyashov/segmentation-unet/tree/59dc95419481b2535a52332e0be92b15c7450674 |
JSD | import math
import torch
from torch import nn
import torch.utils.data
class JSD(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, eps=1e-08):
logN = math.log(float(x.shape[0]))
y = torch.mean(x, 0)
y = y * (y + eps).log() / logN
y = y.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.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
assert_size_stride = torch._... | Joshua-Schroijen/deepproblog | JSD | false | 675 | [
"Apache-2.0"
] | 0 | 4ae56f1e860010b7857b29d5bd76fb1555d5e19d | https://github.com/Joshua-Schroijen/deepproblog/tree/4ae56f1e860010b7857b29d5bd76fb1555d5e19d |
Fusion | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.... | kywen1119/DSRAN | Fusion | false | 15,869 | [
"Apache-2.0"
] | 56 | eb5e515c8d9e527de493f32b62469107a9d398e7 | https://github.com/kywen1119/DSRAN/tree/eb5e515c8d9e527de493f32b62469107a9d398e7 |
MarginCosineProduct | # 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.... | lindsey98/CosFace_pytorch | MarginCosineProduct | false | 10,397 | [
"MIT"
] | 0 | 39bddf763e06c7ccd21fbf45d0c7f1f4a9d8d24d | https://github.com/lindsey98/CosFace_pytorch/tree/39bddf763e06c7ccd21fbf45d0c7f1f4a9d8d24d |
Encoder | import torch
import torch.nn as nn
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=kernel_size // 2, bias=bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | PaParaZz1/DI-engine | Encoder | false | 11,842 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
HighWay | import torch
import torch.nn as nn
from torch.nn import Parameter
class HighWay(torch.nn.Module):
def __init__(self, f_in, f_out, bias=True):
super(HighWay, self).__init__()
self.w = Parameter(torch.Tensor(f_in, f_out))
nn.init.xavier_uniform_(self.w)
if bias:
self.bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 Parameter
assert_size_stride = torch.... | TMUITLab/EAFR | HighWay | false | 1,119 | [
"MIT"
] | 0 | dadb6485d48711ccb8aa2f03760aeb437645f1ff | https://github.com/TMUITLab/EAFR/tree/dadb6485d48711ccb8aa2f03760aeb437645f1ff |
HSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | DYF-AI/openvino-x | HSwish | false | 5,030 | [
"Apache-2.0"
] | 1 | 0f18ebb240ea3394f7e461aca34fac158e686d95 | https://github.com/DYF-AI/openvino-x/tree/0f18ebb240ea3394f7e461aca34fac158e686d95 |
SEBlock | # 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_... | arsalan0004/6DRepNet | SEBlock | false | 14,904 | [
"MIT"
] | 84 | cdfb2b151785eb89fef70907a6f2a19fa0acf4ae | https://github.com/arsalan0004/6DRepNet/tree/cdfb2b151785eb89fef70907a6f2a19fa0acf4ae |
FT | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class FT(nn.Module):
"""
araphrasing Complex Network: Network Compression via Factor Transfer
http://papers.nips.cc/paper/7541-paraphrasing-complex-... | 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... | wangxianliang/FaceX-Zoo | FT | false | 13,085 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | CoraJung/flexible-input-slu | LayerNorm | false | 17,142 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
AlReLU | import torch
from torch import nn
class AlReLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return self.alrelu(input)
def alrelu(self, x):
alpha = 0.01
return torch.maximum(torch.abs(alpha * x), x)
def get_inputs():
return [torch.ran... | 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... | chiranthans23/jigsaw-severity-comments | AlReLU | false | 1,691 | [
"MIT"
] | 0 | b92345ff5bf0c2d2fb243b81edd98adc66a2c4ee | https://github.com/chiranthans23/jigsaw-severity-comments/tree/b92345ff5bf0c2d2fb243b81edd98adc66a2c4ee |
AddCoords | import torch
import torch.nn as nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_size, _,... | 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... | SeunghwanByun/Real-Time-Road-Detection-Network | AddCoords | false | 1,048 | [
"MIT"
] | 0 | bc46615adef0e2b1a9a03dd4951559ca5849e6e1 | https://github.com/SeunghwanByun/Real-Time-Road-Detection-Network/tree/bc46615adef0e2b1a9a03dd4951559ca5849e6e1 |
Classifier | import torch
from torch import nn
from torch.nn import functional as F
class Classifier(nn.Module):
def __init__(self, input_size, hidden_size, n_classes):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, n_classes)
def forwar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ankitkv/pylego | Classifier | false | 18,349 | [
"MIT"
] | 4 | 38d4a8fe8497d748b22c58313cbfd187efb8326e | https://github.com/ankitkv/pylego/tree/38d4a8fe8497d748b22c58313cbfd187efb8326e |
BCELoss2d | # 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... | ArmenGhambaryan/kaggle_carvana_segmentation | BCELoss2d | false | 13,280 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
FSub | import torch
import torch.nn as nn
class FSub(nn.Module):
def __init__(self):
super(FSub, self).__init__()
def forward(self, x, y):
x = x - y - 8.3
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dawnclaude/onnx2keras | FSub | false | 15,144 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
avgpool | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | nathalia-kim/nu_gan | avgpool | false | 10,714 | [
"MIT"
] | 0 | c1d0891945bd7ac3d95869db91f490f57f203110 | https://github.com/nathalia-kim/nu_gan/tree/c1d0891945bd7ac3d95869db91f490f57f203110 |
ContrastiveLoss | import torch
import torch.nn as nn
class ContrastiveLoss(nn.Module):
def __init__(self, margin=0.2):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, imgs, caps):
scores = torch.mm(imgs, caps.t())
diag = scores.diag()
cost_s = torch.clamp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | debayan/dsve-loc | ContrastiveLoss | false | 3,423 | [
"BSD-3-Clause-Clear"
] | 0 | 21b1e1837668b6daa0881514d0756e9bec039fcb | https://github.com/debayan/dsve-loc/tree/21b1e1837668b6daa0881514d0756e9bec039fcb |
Normalize3D | # 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... | CiscoDevNet/vo-id | Normalize3D | false | 17,079 | [
"MIT"
] | 7 | 9a01f866c7539a9cd095d9627ba4f65ad540ea6b | https://github.com/CiscoDevNet/vo-id/tree/9a01f866c7539a9cd095d9627ba4f65ad540ea6b |
TensorConstantLinear | # 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... | Minyus/pipelinex | TensorConstantLinear | false | 14,032 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
ReshapeF | import torch
import torch.utils.data
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out =... | 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.utils.data
import torch
from torch import nn
assert_size_stride = ... | guyii54/Contrastive-I2I | ReshapeF | false | 6,767 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
FusedUpsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedUpsample | false | 11,603 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
SampaddingMaxPool1D | import torch
import torch.nn as nn
class SampaddingMaxPool1D(nn.Module):
def __init__(self, pooling_size, stride):
super(SampaddingMaxPool1D, self).__init__()
self.pooling_size = pooling_size
self.stride = stride
self.padding = nn.ConstantPad1d((int((pooling_size - 1) / 2), int(
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | amoonfana/Knowledge_Distillation | SampaddingMaxPool1D | false | 6,195 | [
"Apache-2.0"
] | 1 | 1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 | https://github.com/amoonfana/Knowledge_Distillation/tree/1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 |
Pow | import torch
class Pow(torch.nn.Module):
def __init__(self):
super(Pow, self).__init__()
def forward(self, x, y):
return x ** y
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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | Pow | false | 2,534 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
Mul | # 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... | yifanpu001/PytorchToCaffe | Mul | false | 4,721 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
EmissionModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.distributions as tdist
assert_size_stri... | ishine/Neural-HMM | EmissionModel | false | 15,621 | [
"MIT"
] | 66 | c0bc23ab88f831173d2d4db29a84503b80c5cdc4 | https://github.com/ishine/Neural-HMM/tree/c0bc23ab88f831173d2d4db29a84503b80c5cdc4 |
CIoU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | jcscheufele/CS545_Final | CIoU | false | 10,238 | [
"MIT"
] | 0 | d86858408a9a0aab82b5d2b7e12847023d939e2e | https://github.com/jcscheufele/CS545_Final/tree/d86858408a9a0aab82b5d2b7e12847023d939e2e |
GraphConvSparse | # 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 numpy as np
import tor... | ksuchoi216/learn-to-cluster | GraphConvSparse | false | 3,939 | [
"MIT"
] | 0 | bef44f92be14e00a96545061a5ecfa7a27da267e | https://github.com/ksuchoi216/learn-to-cluster/tree/bef44f92be14e00a96545061a5ecfa7a27da267e |
EcaModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = ... | Fanzhongjie/ARFE | EcaModule | false | 460 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Bo396543018/mmdetection | NormedLinear | false | 7,786 | [
"Apache-2.0"
] | 16 | eb337336d3c239dc1d20534496f69df41ae9a300 | https://github.com/Bo396543018/mmdetection/tree/eb337336d3c239dc1d20534496f69df41ae9a300 |
SSD300 | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from math import sqrt
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection | SSD300 | false | 1,907 | [
"MIT"
] | 0 | a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 | https://github.com/aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection/tree/a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 |
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