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 |
|---|---|---|---|---|---|---|---|---|---|---|
TinyCnn | # 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_... | Europium248/captum | TinyCnn | false | 438 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
AdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(nn.Module):
def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim,
internal_dim=None):
super(AdditiveAttention, self).__init__()
if internal_dim is None:
internal_dim = i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vision-CAIR/HalentNet | AdditiveAttention | false | 18,075 | [
"MIT"
] | 4 | dedef73c57c63aa580fc497fa42d512f4241a64b | https://github.com/Vision-CAIR/HalentNet/tree/dedef73c57c63aa580fc497fa42d512f4241a64b |
SimpleModel | # 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.... | arashashari/DeepSpeed | SimpleModel | false | 3,130 | [
"MIT"
] | 0 | a2984d0a69640d4cfec4cf38fe22376dc8994a91 | https://github.com/arashashari/DeepSpeed/tree/a2984d0a69640d4cfec4cf38fe22376dc8994a91 |
PreProcess | import torch
import torch.nn as nn
class PreProcess(nn.Module):
def __init__(self):
"""
Blocco di pre-processing delle immagini. Prende il tensore in ingresso nella forma
(batch, width, height, channel), lo permuta e lo normalizza tra 0 e 1.
"""
super(PreProcess, self).__i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | UnibsMatt/NN | PreProcess | false | 2,912 | [
"MIT"
] | 0 | 25eaddc079cd08227a52fdebd95453473fcd3b29 | https://github.com/UnibsMatt/NN/tree/25eaddc079cd08227a52fdebd95453473fcd3b29 |
RefineElasticPendulumModel | # 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 numpy ... | BoyuanChen/neural-state-variables | RefineElasticPendulumModel | false | 7,861 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
InverseDepthSmoothnessLoss | import torch
import torch.nn as nn
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | InverseDepthSmoothnessLoss | false | 278 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
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 import triton_helpers
from torch import nn
assert_s... | TommeyChang/CS294-Homework | Net | false | 1,137 | [
"MIT"
] | 0 | 17b525bf4366034b45c4febd89f1053d44550237 | https://github.com/TommeyChang/CS294-Homework/tree/17b525bf4366034b45c4febd89f1053d44550237 |
UniformBoxWarp | # 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... | PeterouZh/CIPS-3D | UniformBoxWarp | false | 14,166 | [
"MIT"
] | 308 | 9b8bfa0fb23f642af042e150ccd70408f9d137c6 | https://github.com/PeterouZh/CIPS-3D/tree/9b8bfa0fb23f642af042e150ccd70408f9d137c6 |
TransformerEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn import Dropout
from torch.nn import LayerNorm
from typing import Optional
import torch.utils.data
from typing import Tuple
class InProjContainer(torch.nn.Module):
def __init__(self, query_proj, key_proj, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MauiDesign/PyTorchText | TransformerEncoderLayer | false | 9,350 | [
"BSD-3-Clause"
] | 0 | 324c072d55a49bf94da312bc6be893beec3a8bd9 | https://github.com/MauiDesign/PyTorchText/tree/324c072d55a49bf94da312bc6be893beec3a8bd9 |
AvgReadout | import torch
import torch.nn as nn
class AvgReadout(nn.Module):
"""
Considering the efficiency of the method, we simply employ average pooling, computing the average of the set of embedding matrices
.. math::
\\begin{equation}
\\mathbf{H}=\\mathcal{Q}\\left(\\left\\{\\mathbf{H}^{(r)} \\mid ... | 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... | BUPT-GAMMA/OpenHGNN | AvgReadout | false | 13,369 | [
"Apache-2.0"
] | 235 | 5f218dad4ed1415aa6d842bc20785c61e74e5405 | https://github.com/BUPT-GAMMA/OpenHGNN/tree/5f218dad4ed1415aa6d842bc20785c61e74e5405 |
LosslessYCbCr | import torch
import torch.nn.parallel
import torch.utils.data
from torch import nn
import torch.fft
class LosslessYCbCr(nn.Module):
def forward(self, rgb: 'torch.Tensor'):
return torch.cat([(rgb[:, 0:1] + 2 * rgb[:, 1:2] + rgb[:, 2:3]) / 4,
rgb[:, 2:3] - rgb[:, 1:2], rgb[:, 0:1] - rgb[:, 1:2]... | 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.parallel
import torch.utils.data
from torch import nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_s... | KazutakaYamanouchi/bachelor-study | LosslessYCbCr | false | 2,616 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
TinyCnn | # 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_... | LMdeLiangMi/captum | TinyCnn | false | 5,484 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
Delta | # 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
from torchaudio import transforms
assert_size_stride = tor... | albertvillanova/s3prl | Delta | false | 6,166 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
DilatedResidualLayer | # 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_... | Finspire13/Towards-Unified-Surgical-Skill-Assessment | DilatedResidualLayer | false | 8,122 | [
"MIT"
] | 13 | 2c398d4e93889135762e4a91fc4676bfb7706fb0 | https://github.com/Finspire13/Towards-Unified-Surgical-Skill-Assessment/tree/2c398d4e93889135762e4a91fc4676bfb7706fb0 |
ContentLoss | # 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... | RicCu/NeuralStyle | ContentLoss | false | 5,763 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
Attention | import math
import torch
from torch import nn
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, 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.triton_helpers import libdevice
import math
from to... | ChristinaTan0704/transTSP | Attention | false | 297 | [
"MIT"
] | 0 | b97cd7ed8ae97e91b687d5007d13a021781f3d1d | https://github.com/ChristinaTan0704/transTSP/tree/b97cd7ed8ae97e91b687d5007d13a021781f3d1d |
Scale | import torch
import torch.nn as nn
import torch.utils.data
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AmorosTech/RP-R-CNN | Scale | false | 13,251 | [
"MIT"
] | 78 | 45557a69ae9789e2662e3b937feb7624319a3e73 | https://github.com/AmorosTech/RP-R-CNN/tree/45557a69ae9789e2662e3b937feb7624319a3e73 |
AsymmetricLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | youqingxiaozhua/ABAW3 | AsymmetricLoss | false | 11,059 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
BinaryCrossEntropyLoss2d | # 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... | jayden-chua/image-mask | BinaryCrossEntropyLoss2d | false | 3,690 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
Hswish | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hswish(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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
from torch.quantization import QuantStub
from torch.quantization im... | Archermmt/tvm | Hswish | false | 11,194 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
RSubInt | # 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... | bunderhi/torch2trt | RSubInt | false | 1,604 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
HardNegativeContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | maxgreat/dsve-loc | HardNegativeContrastiveLoss | false | 16,047 | [
"BSD-3-Clause-Clear"
] | 56 | dd6807d02c0d5fd3e215be8e5c7a88e73102e561 | https://github.com/maxgreat/dsve-loc/tree/dd6807d02c0d5fd3e215be8e5c7a88e73102e561 |
Interpolate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Interpolate(nn.Module):
def __init__(self):
super(Interpolate, self).__init__()
def forward(self, x):
x = F.interpolate(x, scale_factor=8, mode='nearest', align_corners=None
)
return x
def get_inpu... | 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 | Interpolate | false | 4,714 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
LinearMaxPoolLinearModel | # 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_... | sagnik/captum | LinearMaxPoolLinearModel | false | 4,351 | [
"BSD-3-Clause"
] | 0 | d6b663745ee6c01f072a4358233dec381324c283 | https://github.com/sagnik/captum/tree/d6b663745ee6c01f072a4358233dec381324c283 |
EltwiseSubEmbed | import torch
from torch import nn
class EltwiseSubEmbed(nn.Module):
def __init__(self, nonlinearity='square', use_batch_norm=False,
use_classifier=False, num_features=0, num_classes=0):
super(EltwiseSubEmbed, self).__init__()
self.nonlinearity = nonlinearity
if nonlinearity is not... | 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... | Event0511/curling-reid | EltwiseSubEmbed | false | 17,252 | [
"Apache-2.0"
] | 3 | 1494d0faeed951e495573c694362f001df5bf6fd | https://github.com/Event0511/curling-reid/tree/1494d0faeed951e495573c694362f001df5bf6fd |
MinibatchStdLayer | # 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_... | bg459/gan-ensembling-loader | MinibatchStdLayer | false | 14,960 | [
"MIT"
] | 86 | 5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 | https://github.com/bg459/gan-ensembling-loader/tree/5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 |
Embed | # 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.... | Capetian/FaceX-Zoo | Embed | false | 4,965 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
MultiHeadAttention | import math
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""
Multi-head Self-attention layers, a attention score dropout layer is introduced.
Args:
input_tensor (torch.Tensor): the input of the multi-head self-attention layer
attention_mask (torch.Tensor): the a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dreaming-qin/RecBole | MultiHeadAttention | false | 12,370 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
AuxiliaryConvolutions | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
class AuxiliaryConvolutions(nn.Module):
"""
Additional convolutions to produce higher-level feature maps.
"""
def __init__(self):
super(Auxilia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | adityag6994/pytorch_ssd_training | AuxiliaryConvolutions | false | 3,172 | [
"MIT"
] | 0 | 404f3cbef815e314337ec2c1b4f06a2403a7ce03 | https://github.com/adityag6994/pytorch_ssd_training/tree/404f3cbef815e314337ec2c1b4f06a2403a7ce03 |
ChannelAttentionModule | # 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.... | rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets | ChannelAttentionModule | false | 7,551 | [
"MIT"
] | 1 | 75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 | https://github.com/rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets/tree/75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 |
DiceLoss | import torch
from typing import *
import torch.nn as nn
def dice_coeff(input, target, smooth=1.0):
input_flat = input.view(-1)
target_flat = target.view(-1)
intersection = (input_flat * target_flat).sum()
return (2.0 * intersection + smooth) / (input_flat.sum() + target_flat.
sum() + smooth)
... | 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 typing import *
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | abbiyanaila/torchwisdom | DiceLoss | false | 6,047 | [
"MIT"
] | 1 | 56dc95ebca3f6861c7009cb4fa0c034e260236b1 | https://github.com/abbiyanaila/torchwisdom/tree/56dc95ebca3f6861c7009cb4fa0c034e260236b1 |
FactorizedReduce | # 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 ... | Alison-brie/AutoReg | FactorizedReduce | false | 16,883 | [
"MIT"
] | 10 | a23d45a6f7c6e47f61430e1565dda316452a4418 | https://github.com/Alison-brie/AutoReg/tree/a23d45a6f7c6e47f61430e1565dda316452a4418 |
Critic | import torch
import torch.nn.functional as F
import torch.nn as nn
class Critic(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Critic, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | icml2019-anonymous-author/Action-Robust-Reinforcement-Learning | Critic | false | 6,852 | [
"MIT"
] | 1 | 03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b | https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning/tree/03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b |
BilinearUpsample | # 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 typing import List
from typing import Union
import torch.nn as nn
import torch.utils... | L-Net-1992/DI-engine | BilinearUpsample | false | 5,485 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
HuggingfaceClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | godweiyang/ParaGen | HuggingfaceClassifier | false | 15,441 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
PostPreplayer | # 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.... | abcdefg-dev-dd/asxdcvfg | PostPreplayer | false | 6,075 | [
"Apache-2.0"
] | 1 | 83421d4a133810968d6e04b256a9312895452941 | https://github.com/abcdefg-dev-dd/asxdcvfg/tree/83421d4a133810968d6e04b256a9312895452941 |
ActorNetwork | import torch
import torch.nn.functional as func
class ActorNetwork(torch.nn.Module):
def __init__(self, s_space, a_space):
super(ActorNetwork, self).__init__()
self.first_dense = torch.nn.Linear(s_space, 50)
self.second_dense = torch.nn.Linear(50, a_space)
def forward(self, 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
assert_size_stride = torch._C... | yutiansut/Personae | ActorNetwork | false | 16,777 | [
"MIT"
] | 1,046 | e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | https://github.com/yutiansut/Personae/tree/e5e89cbaaf2c4708952d25fdb25e99837aecdb4e |
StableBCELoss | import torch
import torch.nn as nn
class StableBCELoss(nn.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
input = input.float().view(-1)
target = target.float().view(-1)
neg_abs = -input.abs()
loss = input.clamp(min... | 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
... | toandaominh1997/understanding_cloud_organization | StableBCELoss | false | 4,431 | [
"MIT"
] | 0 | 7da991ff3da557c18f4585c1b956ed799c104c7c | https://github.com/toandaominh1997/understanding_cloud_organization/tree/7da991ff3da557c18f4585c1b956ed799c104c7c |
Rescale | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Catherine0505/mar-scf-flow | Rescale | false | 17,083 | [
"Apache-2.0"
] | 10 | aa7c3564cb9f2967c5e580a633516dba1b597f98 | https://github.com/Catherine0505/mar-scf-flow/tree/aa7c3564cb9f2967c5e580a633516dba1b597f98 |
HardSwish | # 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
import torch.nn.functional as F
assert_size_stride = torch._C._dynam... | L-Net-1992/towhee | HardSwish | false | 13,977 | [
"Apache-2.0"
] | 365 | 471de97bf9c5443efaf3b62fd440b3ebdb6d5903 | https://github.com/L-Net-1992/towhee/tree/471de97bf9c5443efaf3b62fd440b3ebdb6d5903 |
WDLoss | # 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_... | mirmohammad/IFT6135-TP3 | WDLoss | false | 4,016 | [
"MIT"
] | 0 | 70453b4ea695313837ab88243b0206552eb50632 | https://github.com/mirmohammad/IFT6135-TP3/tree/70453b4ea695313837ab88243b0206552eb50632 |
depthwise_block | import torch
import torch.nn as nn
import torch.utils.data
class depthwise_conv(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1):
super(depthwise_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=
stride, padding=padding)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | whiteking64/lang-seg | depthwise_block | false | 16,703 | [
"MIT"
] | 202 | 9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 | https://github.com/whiteking64/lang-seg/tree/9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 |
BertCrossAttention | # 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.... | HS-YN/PanoAVQA | BertCrossAttention | false | 18,390 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
Solver_GAP_OneFClayers | # 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.... | yulinfeng000/AdaptiveNeuralTrees | Solver_GAP_OneFClayers | false | 13,159 | [
"MIT"
] | 0 | bbcb381b9cb0c91ae1af33ce43b43f352055041c | https://github.com/yulinfeng000/AdaptiveNeuralTrees/tree/bbcb381b9cb0c91ae1af33ce43b43f352055041c |
Sign | from torch.autograd import Function
import torch
import torch.nn as nn
class SignFunction(Function):
def __init__(self):
super(SignFunction, self).__init__()
@staticmethod
def forward(ctx, input, is_training=True):
if is_training:
prob = input.new(input.size()).uniform_()
... | 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.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | Biaze7/lossy-image-compression | Sign | false | 7,774 | [
"MIT"
] | 16 | 88ca2022a306fea52d6671593b314f0de3bf6010 | https://github.com/Biaze7/lossy-image-compression/tree/88ca2022a306fea52d6671593b314f0de3bf6010 |
PEGCNLayer | # 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.... | snap-stanford/distance-encoding | PEGCNLayer | false | 16,498 | [
"MIT"
] | 177 | b9ccb1b59422b11b40883d0284d7fc9ba88efdb6 | https://github.com/snap-stanford/distance-encoding/tree/b9ccb1b59422b11b40883d0284d7fc9ba88efdb6 |
MatrixReduceSum | # 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.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixReduceSum | false | 14,353 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch import optim as optim
assert_size_stride = torc... | billpsomas/ibot | PatchEmbed | false | 1,570 | [
"Apache-2.0"
] | 0 | c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d | https://github.com/billpsomas/ibot/tree/c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d |
NormalAttention_dot | # 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 ... | Schwartz-Zha/My-invertible-resnet | NormalAttention_dot | false | 1,037 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.channels = channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | vanthq/EarRecognition | ResidualBlock | false | 10,942 | [
"MIT"
] | 0 | 7decddc97c4b27cd8457308b3d3836388936e7a8 | https://github.com/vanthq/EarRecognition/tree/7decddc97c4b27cd8457308b3d3836388936e7a8 |
CmapPafHead | # 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
import torch.optim
assert_size_stride = ... | KeithStoke/POSE_Test | CmapPafHead | false | 9,214 | [
"MIT"
] | 0 | 581aaf6f3d4fd50e56aa16c43913292af7d36879 | https://github.com/KeithStoke/POSE_Test/tree/581aaf6f3d4fd50e56aa16c43913292af7d36879 |
NormedLinear | # 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.... | Jianf-Wang/RSG | NormedLinear | false | 13,902 | [
"MIT"
] | 108 | 3c5074511455428d81af89e1621493dcdb5db6ce | https://github.com/Jianf-Wang/RSG/tree/3c5074511455428d81af89e1621493dcdb5db6ce |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwanChung/tapm | Block | false | 8,379 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
TransformerEncoderLayer | import torch
import torch.nn as nn
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | imperial-qore/CAROL | TransformerEncoderLayer | false | 6,872 | [
"BSD-3-Clause"
] | 1 | 57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 | https://github.com/imperial-qore/CAROL/tree/57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 |
MultiplyLuminance | import torch
import torch.nn
class MultiplyLuminance(torch.nn.Module):
def __init__(self):
super(MultiplyLuminance, self).__init__()
def forward(self, color, luminance):
return color * (1 + luminance)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def g... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ashwinpn/Computer-Vision | MultiplyLuminance | false | 6,252 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
LocationNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class LocationNetwork(nn.Module):
"""
Uses the internal state `h_t` of the core network to
produce the location coordinates `l_t` for the next
time step.
Concretely, feeds the hidden state `h_t` through a fc
layer followed by ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | reinvantveer/topography-detection | LocationNetwork | false | 10,706 | [
"MIT"
] | 0 | b471dbaa1bc276584374ed3bb5382e2d63046611 | https://github.com/reinvantveer/topography-detection/tree/b471dbaa1bc276584374ed3bb5382e2d63046611 |
MultiHeadedAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.0):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
self.d_k = d_model // h
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.... | SSussexGit/deepikachu | MultiHeadedAttention | false | 2,812 | [
"MIT"
] | 0 | 72999c4a3f1767c3e5f332fe64cba9240ef43a79 | https://github.com/SSussexGit/deepikachu/tree/72999c4a3f1767c3e5f332fe64cba9240ef43a79 |
Loss | import torch
import torch as t
import torch.nn as nn
def indicator(K):
"""
@K: number of users
"""
return t.eye(5 * K)
class Loss(nn.Module):
def __init__(self, K, Nt, Vartheta):
super(Loss, self).__init__()
self.K = K
self.Nt = Nt
self.Delta = indica... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SoulVen/USRMNet-HWGCN | Loss | false | 1,079 | [
"Apache-2.0"
] | 0 | 2f99f53150335be26270bd408ce59dc51c8435cc | https://github.com/SoulVen/USRMNet-HWGCN/tree/2f99f53150335be26270bd408ce59dc51c8435cc |
AdaptiveAvgPool3dOutSize1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
from abc import abstractmethod
from typing import Tuple
import torch.nn
assert_size_stride = t... | denred0/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 1,828 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 |
conv_head_pooling | # 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.data
assert_size_stride = torch._C._dyn... | newstzpz/d2go | conv_head_pooling | false | 12,830 | [
"Apache-2.0"
] | 0 | fcd511714ec4e34040d35379cb0382b70fb58c70 | https://github.com/newstzpz/d2go/tree/fcd511714ec4e34040d35379cb0382b70fb58c70 |
Conv_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv_Q(nn.Module):
def __init__(self, frames, num_actions):
super(Conv_Q, self).__init__()
self.c1 = nn.Conv2d(frames, 32, kernel_size=8, stride=4)
self.c2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
self.c3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cedesu/BCQ | Conv_Q | false | 12,212 | [
"MIT"
] | 0 | 424548510349a85c31809431494dcc6f64b611ba | https://github.com/cedesu/BCQ/tree/424548510349a85c31809431494dcc6f64b611ba |
InnerProductModel | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | SheepiesLab/plato | InnerProductModel | false | 12,079 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 |
GaussianKLLoss | import torch
import torch.nn as nn
class GaussianKLLoss(nn.Module):
def __init__(self):
super(GaussianKLLoss, self).__init__()
def forward(self, mu1, logvar1, mu2, logvar2):
numerator = logvar1.exp() + torch.pow(mu1 - mu2, 2)
fraction = torch.div(numerator, logvar2.exp())
kl ... | 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... | kekayan/Info-HCVAE | GaussianKLLoss | false | 15,802 | [
"Apache-2.0"
] | 120 | 1f4d536523767f439e689d8963c54a55fb75c6f9 | https://github.com/kekayan/Info-HCVAE/tree/1f4d536523767f439e689d8963c54a55fb75c6f9 |
ELUPlus | # 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
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | MilesCranmer/nflows | ELUPlus | false | 11,703 | [
"MIT"
] | 0 | 6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 | https://github.com/MilesCranmer/nflows/tree/6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 |
Net_BP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Tappai/PV_prediction | Net_BP | false | 5,874 | [
"Apache-2.0"
] | 1 | 2ff1e1af183a28f07ebc2ec2979488eb8e246813 | https://github.com/Tappai/PV_prediction/tree/2ff1e1af183a28f07ebc2ec2979488eb8e246813 |
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
import torch.nn as nn
import torch.multiprocessing
assert_size_stride = torch._C... | AppleHolic/FastSpeech2 | Conv | false | 16,937 | [
"MIT"
] | 8 | 8f6969edd0c86c05b1dd70a0b7841bd86505455e | https://github.com/AppleHolic/FastSpeech2/tree/8f6969edd0c86c05b1dd70a0b7841bd86505455e |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | J-zin/SNUH | Decoder | false | 17,448 | [
"MIT"
] | 4 | e4bde66609e1480f890b8386046431d488b825bd | https://github.com/J-zin/SNUH/tree/e4bde66609e1480f890b8386046431d488b825bd |
ResidualDenseBlock_3C | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
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
from torch.nn import Parameter
assert_size_stride = torch.... | LenKerr/Semantic-Colorization-GAN | ResidualDenseBlock_3C | false | 5,536 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
ConvAttentionLayer | import math
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torch.onnx.operators
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | PeterouZh/SemiNAS | ConvAttentionLayer | false | 17,818 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
CBAM_Module | import torch
from torch import nn
from torchvision.transforms import *
class CBAM_Module(nn.Module):
def __init__(self, channels, reduction):
super(CBAM_Module, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | IrvingShu/batch-feature-erasing-network | CBAM_Module | false | 13,864 | [
"MIT"
] | 152 | 534616c09dade92561a0203797892a63a072b1b4 | https://github.com/IrvingShu/batch-feature-erasing-network/tree/534616c09dade92561a0203797892a63a072b1b4 |
GraphAttentionLayer | # 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.... | dawnranger/pytorch-AGNN | GraphAttentionLayer | false | 15,164 | [
"MIT"
] | 137 | 461f71b45e5eaddb50cff31a537b06cb1a50ba8f | https://github.com/dawnranger/pytorch-AGNN/tree/461f71b45e5eaddb50cff31a537b06cb1a50ba8f |
SinkhornDivergence | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DMIRLAB-Group/Dassl.pytorch | SinkhornDivergence | false | 5,037 | [
"MIT"
] | 1 | 79052448cc0b0622f14e9768dbd6e6c0598fe6d1 | https://github.com/DMIRLAB-Group/Dassl.pytorch/tree/79052448cc0b0622f14e9768dbd6e6c0598fe6d1 |
WeightedBDiceLoss | import torch
import torch.nn.functional
import torch.nn as nn
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class WeightedBDiceLoss(nn.Module):
def... | 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.functional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | HelenGuohx/cv-ferattn-code | WeightedBDiceLoss | false | 5,289 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
GatedConv1d | import torch
import torch.nn as nn
class MaskedConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
groups=1, bias=True, causal=True):
if causal:
padding = (kernel_size - 1) * dilation
else:
padding = (kernel_size - 1) * dilatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Raiselimit/TorchBlocks | GatedConv1d | false | 5,744 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
Smooth_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SeokjaeLIM/DSLR-release | Smooth_loss | false | 8,753 | [
"Apache-2.0"
] | 14 | 861429482faf50ee3d6570948af8c48df1fc7f43 | https://github.com/SeokjaeLIM/DSLR-release/tree/861429482faf50ee3d6570948af8c48df1fc7f43 |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, noise=None):
if noise is None:
batch, _, height, width = image.shape
noise = image.new... | 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... | BinahHu/stylegan2-pytorch | NoiseInjection | false | 165 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 9975707ffd93872fce02f7e3654eb588a09e23e4 | https://github.com/BinahHu/stylegan2-pytorch/tree/9975707ffd93872fce02f7e3654eb588a09e23e4 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | v01dXYZ/petastorm | Net | false | 13,073 | [
"Apache-2.0"
] | 0 | d6f4e82eb2c3a6c2b4c16c060c7350331b60a51a | https://github.com/v01dXYZ/petastorm/tree/d6f4e82eb2c3a6c2b4c16c060c7350331b60a51a |
DQN_xy2 | # 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 ... | CoAxLab/azad | DQN_xy2 | false | 17,183 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
MLP | # 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.... | Cjkkkk/nnfusion | MLP | false | 8,911 | [
"MIT"
] | 0 | 7ee61dfdd66fbf67eb178fcc5cfa1cddb99b3c13 | https://github.com/Cjkkkk/nnfusion/tree/7ee61dfdd66fbf67eb178fcc5cfa1cddb99b3c13 |
BasicModel2 | # 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... | LMdeLiangMi/captum | BasicModel2 | false | 5,469 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
LandmarkHead | # 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
from itertools import product as produ... | Capetian/FaceX-Zoo | LandmarkHead | false | 5,026 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
AttentionLayer | import torch
import torch.nn as nn
import torch.onnx
import torch.nn.functional as F
class AttentionLayer(nn.Module):
"""
Attention layer according to https://arxiv.org/abs/1409.0473.
Params:
num_units: Number of units used in the attention layer
"""
def __init__(self, query_size, key_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | AttentionLayer | false | 3,781 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
BilinearMatrixAttention | import torch
import torch.nn as nn
class BilinearMatrixAttention(nn.Module):
"""
Adopted from AllenNLP. For now there is no activation function
"""
def __init__(self, matrix_1_dim: 'int', matrix_2_dim: 'int',
use_input_biases: 'bool'=False, label_dim: 'int'=1) ->None:
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Impavidity/relogic | BilinearMatrixAttention | false | 8,292 | [
"MIT"
] | 24 | f647106e143cd603b95b63e06ea530cdd516aefe | https://github.com/Impavidity/relogic/tree/f647106e143cd603b95b63e06ea530cdd516aefe |
BertPooler | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Adoni/pytorch-pretrained-BERT | BertPooler | false | 3,485 | [
"Apache-2.0"
] | 0 | 845c33f00e933626dcfc96e0923ecf034295ef75 | https://github.com/Adoni/pytorch-pretrained-BERT/tree/845c33f00e933626dcfc96e0923ecf034295ef75 |
AbsModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | mirecta/nncase | AbsModule | false | 4,166 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
Convolution | # 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_... | Baymine/Dassl | Convolution | false | 11,242 | [
"MIT"
] | 0 | 0836fb1f08393e2204326618e783d796741f657e | https://github.com/Baymine/Dassl/tree/0836fb1f08393e2204326618e783d796741f657e |
M3 | # 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_... | Juggernaut93/SSH-pytorch | M3 | false | 13,943 | [
"MIT"
] | 63 | 8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 | https://github.com/Juggernaut93/SSH-pytorch/tree/8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 |
PyConv2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | lkf59553/pyconv | PyConv2 | false | 15,943 | [
"MIT"
] | 295 | d8b39cf43014b8fd277dcefc9eb7f8880511e977 | https://github.com/lkf59553/pyconv/tree/d8b39cf43014b8fd277dcefc9eb7f8880511e977 |
PositionwiseFeedForward | # 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.... | MarcosPampuch/TDNet_CARLA | PositionwiseFeedForward | false | 810 | [
"MIT"
] | 0 | efc1c872966f1cef49b82723170586a6abcfb524 | https://github.com/MarcosPampuch/TDNet_CARLA/tree/efc1c872966f1cef49b82723170586a6abcfb524 |
KnowledgeDistillationLoss | # 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
... | VitoPalmisano/MiB_BiSeNet_SEAM_test | KnowledgeDistillationLoss | false | 2,946 | [
"MIT"
] | 0 | 7b74beb69f135c0bb843ee24c90c3097ce448eec | https://github.com/VitoPalmisano/MiB_BiSeNet_SEAM_test/tree/7b74beb69f135c0bb843ee24c90c3097ce448eec |
MLP_LeNetMNIST | # 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.... | yulinfeng000/AdaptiveNeuralTrees | MLP_LeNetMNIST | false | 13,164 | [
"MIT"
] | 0 | bbcb381b9cb0c91ae1af33ce43b43f352055041c | https://github.com/yulinfeng000/AdaptiveNeuralTrees/tree/bbcb381b9cb0c91ae1af33ce43b43f352055041c |
Vol | import math
import torch
from torch import Tensor
import torchaudio.functional as F
class Vol(torch.nn.Module):
"""Add a volume to an waveform.
Args:
gain (float): Interpreted according to the given gain_type:
If ``gain_type`` = ``amplitude``, ``gain`` is a positive amplitude ratio.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Nayef211/audio | Vol | false | 11,737 | [
"BSD-2-Clause"
] | 0 | 241ab1e8284e589262f510ee9411baf2bc374ded | https://github.com/Nayef211/audio/tree/241ab1e8284e589262f510ee9411baf2bc374ded |
NeuralNetNonDifferentiableOutput | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetNonDifferentiableOutput(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNonDifferentiableOutput, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import torch.... | TingGong1/onnxruntime | NeuralNetNonDifferentiableOutput | false | 5,902 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
BasePolicy | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasePolicy(nn.Module):
"""
Base policy network
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.
leaky_relu, norm_in=False, onehot_dim=0):
"""
Inputs:
input_dim (int): Number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | pohanchi/DL_final_project | BasePolicy | false | 7,480 | [
"Apache-2.0"
] | 1 | 8ade422f61a2e8bd4256523ebda56e19b189fe91 | https://github.com/pohanchi/DL_final_project/tree/8ade422f61a2e8bd4256523ebda56e19b189fe91 |
L2Norm | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | Coral-SH/TextBoxes_PyTorch | L2Norm | false | 17,136 | [
"MIT"
] | 8 | fb1636139d69e762b567a234c3a4b69e3dd43071 | https://github.com/Coral-SH/TextBoxes_PyTorch/tree/fb1636139d69e762b567a234c3a4b69e3dd43071 |
UNet | # 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.functional import ... | delldu/VideoSlow | UNet | false | 6,711 | [
"MIT"
] | 1 | 2badbbfa2187ea15ea37ef35e70a103ef98c1e33 | https://github.com/delldu/VideoSlow/tree/2badbbfa2187ea15ea37ef35e70a103ef98c1e33 |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | msank00/miniTransformer | DecoderLayer | false | 12,831 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
GAP1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._... | VincentSch4rf/torchtime | GAP1d | false | 18,039 | [
"Apache-2.0"
] | 4 | bebd006cd67b31c342e0658285c9771c27411df0 | https://github.com/VincentSch4rf/torchtime/tree/bebd006cd67b31c342e0658285c9771c27411df0 |
SirenLayer | import math
import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0=30.0):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
class SirenLayer(nn.Module):
def __init__(self, input_dim, hidden_dim, use_bias=True, w0=1.0,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ZixiuHuang/nex-code | SirenLayer | false | 3,001 | [
"MIT"
] | 0 | c9432fb675914391b4de4786220351a0dc35aecb | https://github.com/ZixiuHuang/nex-code/tree/c9432fb675914391b4de4786220351a0dc35aecb |
GCN | import torch
import torch.nn as nn
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else act
if bias:
self.bias = nn.Parameter(tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | CrowdDynamicsLab/InfoMotif | GCN | false | 17,172 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
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