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 |
|---|---|---|---|---|---|---|---|---|---|---|
PolicyNetwork | # 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.... | Dookas/Robust-Multitask-RL | PolicyNetwork | false | 13,610 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
NetTan2018 | # 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_... | Nicolik/SimpleCNNClassifier | NetTan2018 | false | 8,616 | [
"MIT"
] | 11 | e5cd37fbde90f4096183658abe3f8836be92a8f2 | https://github.com/Nicolik/SimpleCNNClassifier/tree/e5cd37fbde90f4096183658abe3f8836be92a8f2 |
RSELayer | # 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 ... | verages/PaddleOCR2Pytorch | RSELayer | false | 4,667 | [
"Apache-2.0"
] | 0 | 201f0d5d6007f49620c49af7d222c3b220eb3e70 | https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70 |
GatedConv1d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch
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
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
a... | B0BBB/seq2seq.pytorch | GatedConv1d | false | 108 | [
"MIT"
] | 0 | 54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 | https://github.com/B0BBB/seq2seq.pytorch/tree/54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 |
Analysis_net_17 | # 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.... | Geunwoo-Jeon/iclr_17_compression | Analysis_net_17 | false | 13,795 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
VPReLU | # 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... | DucNguyen183/nfnet_f5 | VPReLU | false | 13,591 | [
"Apache-2.0"
] | 133 | 567a1126ff6ea09b33ffa5dacfac9c983fd48713 | https://github.com/DucNguyen183/nfnet_f5/tree/567a1126ff6ea09b33ffa5dacfac9c983fd48713 |
FThreshold | # 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | FThreshold | false | 15,141 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
MLP | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/diaparser | MLP | false | 9,247 | [
"MIT"
] | 0 | ca11e65ef890cee2fbb23f42ae9c711c89767158 | https://github.com/KoichiYasuoka/diaparser/tree/ca11e65ef890cee2fbb23f42ae9c711c89767158 |
CombFilter | # 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... | BaiYuhaoSpiceeYJ/SEGAN_denoise | CombFilter | false | 2,013 | [
"MIT"
] | 0 | 5bf65ae72b9f0a996ae338c53c68c4967e08cd59 | https://github.com/BaiYuhaoSpiceeYJ/SEGAN_denoise/tree/5bf65ae72b9f0a996ae338c53c68c4967e08cd59 |
RNNAgent | # 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_... | Sud0x67/mrmix | RNNAgent | false | 18,379 | [
"Apache-2.0"
] | 4 | 4f4784e421c768509bd007e21b4455b56edc7cd2 | https://github.com/Sud0x67/mrmix/tree/4f4784e421c768509bd007e21b4455b56edc7cd2 |
ProbabilityBilinear | import torch
import torch.nn as nn
import torch.nn.functional as F
def normalize_prob(a, dim=-1):
"""Perform 1-norm along the specific dimension."""
return a / a.sum(dim=dim, keepdim=True)
class ProbabilityBilinear(nn.Bilinear):
def __init__(self, in1_features, in2_features, out_features, bias=False,
... | 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
... | ashutosh1919/neuro-symbolic-sudoku-solver | ProbabilityBilinear | false | 14,907 | [
"Apache-2.0"
] | 52 | ecb4274ff66d3b6a86f64584e0a767bf785f107f | https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver/tree/ecb4274ff66d3b6a86f64584e0a767bf785f107f |
NTXent | import torch
import torch.nn as nn
import torch.nn.functional as F
class NTXent(nn.Module):
def __init__(self, metric: 'str'='CosineSimilarity', temperature:
'float'=0.5, reduction: 'str'='mean'):
super().__init__()
if metric not in ['CosineSimilarity']:
raise ValueError('Unde... | 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... | Meteor-han/ReLMole | NTXent | false | 5,592 | [
"MIT"
] | 1 | ec8f2d3ec7b8edb6cd34aede36a980bab3dc35c2 | https://github.com/Meteor-han/ReLMole/tree/ec8f2d3ec7b8edb6cd34aede36a980bab3dc35c2 |
SoftmaxLoss | # 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.... | AyushExel/s3prl | SoftmaxLoss | false | 1,987 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
TransformerDecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class TransformerDecoderLayer(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cimeister/neural-transducer | TransformerDecoderLayer | false | 1,746 | [
"MIT"
] | 0 | e4dfc718bbcf93254ce23750e5428c5131ddfb98 | https://github.com/cimeister/neural-transducer/tree/e4dfc718bbcf93254ce23750e5428c5131ddfb98 |
CoralLayer | # 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... | Dineswar11/dretino | CoralLayer | false | 8,964 | [
"MIT"
] | 0 | f6b1e1043a62f88b1853df1bfaada296710223f7 | https://github.com/Dineswar11/dretino/tree/f6b1e1043a62f88b1853df1bfaada296710223f7 |
Detector | import torch
from torch import nn
import torch.nn.functional as F
class Detector(nn.Module):
def __init__(self):
super(Detector, self).__init__()
self.conv1 = nn.Conv2d(50, 50, 3, 1, 1)
self.conv2 = nn.Conv2d(50, 50, 3, 1, 1)
self.fc1 = nn.Linear(2 * 2 * 50, 50)
self.fc2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ksivaman/observer-networks | Detector | false | 7,065 | [
"MIT"
] | 1 | a0cd540c762751c5500f714dc3979d3a62b9ea77 | https://github.com/ksivaman/observer-networks/tree/a0cd540c762751c5500f714dc3979d3a62b9ea77 |
SimplePowModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimplePowModule(torch.nn.Module):
def __init__(self, power):
super(SimplePowModule, self).__init__()
self.power = power
def forward(self, tensor):
return torch.pow(tensor, self.power)
def get_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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimplePowModule | false | 3,352 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SchedulerTestNet | # 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... | Benjamin-Etheredge/lightning-bolts | SchedulerTestNet | false | 157 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Fpiotro/MOLECULAR-TRANSLATION | Attention | false | 17,298 | [
"MIT"
] | 5 | 050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 | https://github.com/Fpiotro/MOLECULAR-TRANSLATION/tree/050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 |
MonomialNN | # 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
from warnings import warn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._... | DiffEqML/neurodiffeq | MonomialNN | false | 5,069 | [
"MIT"
] | 1 | c5e7404c47a4729578ee2149f289be0a8909d775 | https://github.com/DiffEqML/neurodiffeq/tree/c5e7404c47a4729578ee2149f289be0a8909d775 |
ReCodeAlphabet | # 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... | Luma-1994/lama | ReCodeAlphabet | false | 14,000 | [
"MIT"
] | 137 | 60d802e2e4cce789f03eea11b038212ba5f7fd1b | https://github.com/Luma-1994/lama/tree/60d802e2e4cce789f03eea11b038212ba5f7fd1b |
SimplifiedScaledDotProductAttention | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | SimplifiedScaledDotProductAttention | false | 4,224 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
HSwish | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSwish(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... | 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.nn.functional
import torch.nn.parallel
import torch.ut... | ardianumam/Vanilla-GAN | HSwish | false | 12,102 | [
"Apache-2.0"
] | 0 | 3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | https://github.com/ardianumam/Vanilla-GAN/tree/3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 |
LearnMaskedDefault | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the defaul... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.nn
assert_size_stride = torch.... | zijian-hu/pytorchvideo | LearnMaskedDefault | false | 4,699 | [
"Apache-2.0"
] | 0 | 51589b100437af2285c56ce2ccc7ccecb7f9b18b | https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b |
SoftL1 | # 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
assert_size_stride = t... | Tiamat-Tech/npms | SoftL1 | false | 14,509 | [
"MIT"
] | 96 | 2d1bce8c98b0f24aa69273975c52b2fbdb101c29 | https://github.com/Tiamat-Tech/npms/tree/2d1bce8c98b0f24aa69273975c52b2fbdb101c29 |
ScaledDotProductGeometryAttention | # 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.... | jianqingxie/RSTNet | ScaledDotProductGeometryAttention | false | 15,693 | [
"BSD-3-Clause"
] | 68 | aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be | https://github.com/jianqingxie/RSTNet/tree/aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be |
Actor | # 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.... | Manojbhat09/Sane-annotation-shape-complete | Actor | false | 17,703 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
MulticlassDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | lee-zq/VesselSeg-pytorch | MulticlassDiceLoss | false | 15,906 | [
"Apache-2.0"
] | 83 | b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa | https://github.com/lee-zq/VesselSeg-pytorch/tree/b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa |
USConv2d | import torch
import torch.nn as nn
import torch.utils
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/ 0344c5503ee55e24f0de7f37336a6e08f10976fd/ research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is None:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | Gaussianer/FasterSeg | USConv2d | false | 17,299 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
Sin | import torch
from torch import nn
class Sin(nn.Module):
"""An element-wise sin activation wrapped as a nn.Module.
Shape:
- Input: `(N, *)` where `*` means, any number of additional dimensions
- Output: `(N, *)`, same shape as the input
Examples:
>>> m = Sin()
>>> input = torch.randn(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_... | HJReachability/safety_rl | Sin | false | 17,329 | [
"BSD-3-Clause"
] | 5 | 00b441b41cea2a5062ffdc4ac30903b51364c2f9 | https://github.com/HJReachability/safety_rl/tree/00b441b41cea2a5062ffdc4ac30903b51364c2f9 |
BehlerAngular | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | jduerholt/schnetpack | BehlerAngular | false | 6,932 | [
"MIT"
] | 1 | 228d50fdeba4592b1de54d3a9570d766757c2ee1 | https://github.com/jduerholt/schnetpack/tree/228d50fdeba4592b1de54d3a9570d766757c2ee1 |
focal_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | miguel-fc/atomai | focal_loss | false | 10,661 | [
"MIT"
] | 0 | f51699ef5e1bfc577781977d38f7414b1b51449d | https://github.com/miguel-fc/atomai/tree/f51699ef5e1bfc577781977d38f7414b1b51449d |
ProjectExciteLayer | # 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_... | YilinLiu97/AmygNet-Pytorch | ProjectExciteLayer | false | 18,146 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
MultiheadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | farhanazareen/Fairseq | MultiheadAttention | false | 6,694 | [
"BSD-3-Clause"
] | 1 | 39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 | https://github.com/farhanazareen/Fairseq/tree/39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 |
SoftWingLoss | import math
import torch
import torch.nn as nn
class SoftWingLoss(nn.Module):
"""Soft Wing Loss 'Structure-Coherent Deep Feature Learning for Robust Face
Alignment' Lin et al. TIP'2021.
loss =
1. |x| , if |x| < omega1
2. omega2*ln(1+|x|/epsilon) + B, if |x| >= om... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | atoaiari/mmpose | SoftWingLoss | false | 6,289 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
ToSEG | from torch.autograd import Function
import math
import torch
import warnings
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.cpp_extension
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if input.device.type == 'cpu':
if bias is not 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.autograd import Function
import math
import warnings
import numpy as ... | crobbins327/semanticGAN_WSI | ToSEG | false | 1,813 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 |
Network | import torch
class Network(torch.nn.Module):
def __init__(self, input_dimension, output_dimension):
super(Network, self).__init__()
self.layer_1 = torch.nn.Linear(in_features=input_dimension,
out_features=100)
self.layer_2 = torch.nn.Linear(in_features=100, out_features=200)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World- | Network | false | 6,739 | [
"MIT"
] | 1 | f32ceac5f4e29cba212d6fd1b8a25c08ac733666 | https://github.com/georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World-/tree/f32ceac5f4e29cba212d6fd1b8a25c08ac733666 |
GraphConv | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class MeanAggregator(nn.Module):
def forward(self, features, A):
x = torch.bmm(A, features)
return x
class GraphConv(nn.Module):
def __init__(self, in_dim, out_dim):
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | HolyCrap96/mmocr-1 | GraphConv | false | 9,213 | [
"Apache-2.0"
] | 0 | c6c4acd39b1c56fec1b87530b2d241fe8af4ceed | https://github.com/HolyCrap96/mmocr-1/tree/c6c4acd39b1c56fec1b87530b2d241fe8af4ceed |
SemanticComposite | import torch
import torch.nn as nn
class SemanticComposite(nn.Module):
"""
SemanticComposite module.
Apply a self-attention layer and a semantic composite fuse gate to compute the
encoding result of one tensor.
:param in_features: Feature size of input.
:param dropout_rate: The dropout rate.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ThuYShao/MatchZoo-py | SemanticComposite | false | 11,986 | [
"Apache-2.0"
] | 0 | dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 | https://github.com/ThuYShao/MatchZoo-py/tree/dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 |
SELayer | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.onnx
from torch.optim.lr_scheduler import *
def composite_swish(inputs_1, inputs_2):
return inputs_1 * torch.sigmoid(inputs_2)
def swish(x):
return torch.sigmoid(x) * x
class _Conv2dSamePadding(nn.Conv2d):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch import nn
from torch.nn import functional as F
import tor... | IST-DASLab/ACDC | SELayer | false | 17,432 | [
"Apache-2.0"
] | 6 | ac53210b6adc1f2506ff909de08172ed9cad25d5 | https://github.com/IST-DASLab/ACDC/tree/ac53210b6adc1f2506ff909de08172ed9cad25d5 |
BinaryReg | # 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
... | divyam-goel/pytorch_connectomics | BinaryReg | false | 6,572 | [
"MIT"
] | 1 | a2c70a7cc60fd84d67be6f225c123ff11daadb83 | https://github.com/divyam-goel/pytorch_connectomics/tree/a2c70a7cc60fd84d67be6f225c123ff11daadb83 |
CeilModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | mirecta/nncase | CeilModule | false | 4,172 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
FusedLeakyReLU | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slop... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asse... | bronemos/contrastive-unpaired-translation-focal | FusedLeakyReLU | false | 3,249 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
core_network | import torch
import torch.nn as nn
import torch.nn.functional as F
class core_network(nn.Module):
"""
An RNN that maintains an internal state that integrates
information extracted from the history of past observations.
It encodes the agent's knowledge of the environment through
a state vector `h_t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | felixnon/foveated-visual-attention | core_network | false | 12,366 | [
"MIT"
] | 0 | 7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 | https://github.com/felixnon/foveated-visual-attention/tree/7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 |
QueryModule | # 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... | kdexd/probnmn-clevr | QueryModule | false | 15,793 | [
"MIT"
] | 69 | 9c1b2286cf30e9fb045370153c9242a39760e02e | https://github.com/kdexd/probnmn-clevr/tree/9c1b2286cf30e9fb045370153c9242a39760e02e |
WordPredictor | # 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... | ROCmSoftwarePlatform/translate | WordPredictor | false | 976 | [
"BSD-3-Clause"
] | 0 | 32a6380d914ebe1a6c38c4992aac9600ed3d9810 | https://github.com/ROCmSoftwarePlatform/translate/tree/32a6380d914ebe1a6c38c4992aac9600ed3d9810 |
NLKProjection | # 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
import t... | HKUST-KnowComp/EFO-1-QA-benchmark | NLKProjection | false | 17,357 | [
"MIT"
] | 9 | 600fb02c76ab631f93ee362ceb789216ec085790 | https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark/tree/600fb02c76ab631f93ee362ceb789216ec085790 |
HighwayNetwork | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
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 import nn
import t... | Rexiome/NATSpeech | HighwayNetwork | false | 14,294 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
StateInitZero | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class StateInitZero(nn.Module):
def __init__(self, hidden_size, num_layers=1, batch_first=False):
super(StateInitZero, self).__init__()
self.hidden_size = hidden_size
self.num_laye... | 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
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | krodyush/training_extensions | StateInitZero | false | 10,982 | [
"Apache-2.0"
] | 0 | 542f4004dfbc6fc62a622065367ba4f85a703dd3 | https://github.com/krodyush/training_extensions/tree/542f4004dfbc6fc62a622065367ba4f85a703dd3 |
EqualConvTranspose2d | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | g33sean/RTIL | EqualConvTranspose2d | false | 6,715 | [
"BSD-2-Clause",
"MIT"
] | 1 | 5325f6d5e3ddf7579b6bd8199898e00eff3da631 | https://github.com/g33sean/RTIL/tree/5325f6d5e3ddf7579b6bd8199898e00eff3da631 |
Conv2dWithConstraint | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ErikBjare/braindecode | Conv2dWithConstraint | false | 411 | [
"BSD-3-Clause"
] | 0 | d77788d4813d353b65f0bfc61c6129015b0390b4 | https://github.com/ErikBjare/braindecode/tree/d77788d4813d353b65f0bfc61c6129015b0390b4 |
FreqEncoder | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | wx-b/torch-ngp | FreqEncoder | false | 10,948 | [
"MIT"
] | 0 | b5799e90dca4e188b14f8c77abf0d420c0bac915 | https://github.com/wx-b/torch-ngp/tree/b5799e90dca4e188b14f8c77abf0d420c0bac915 |
ChannelNorm | import torch
import torch.nn as nn
class ChannelNorm(nn.Module):
def __init__(self, numFeatures, epsilon=1e-05, affine=True):
super(ChannelNorm, self).__init__()
if affine:
self.weight = nn.parameter.Parameter(torch.Tensor(1,
numFeatures, 1))
self.bias = nn... | 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_... | raphaelreme/CPC_audio | ChannelNorm | false | 10,837 | [
"MIT"
] | 0 | a2b045d5f03f4a73beaab9b481244e454edacbaa | https://github.com/raphaelreme/CPC_audio/tree/a2b045d5f03f4a73beaab9b481244e454edacbaa |
Channel_mean | # 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... | wk989898/ARES-implement | Channel_mean | false | 4,536 | [
"MIT"
] | 0 | b2411be01124feaccbc89d74f6025fbfa584bb3f | https://github.com/wk989898/ARES-implement/tree/b2411be01124feaccbc89d74f6025fbfa584bb3f |
Conv1d | # 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... | Sala7efelninja/GST-Tacotron | Conv1d | false | 11,853 | [
"MIT"
] | 0 | e69a5663832a2c3639d4afbb85092a35be621380 | https://github.com/Sala7efelninja/GST-Tacotron/tree/e69a5663832a2c3639d4afbb85092a35be621380 |
SimpleConvNetBlock | import torch
import torch.nn as nn
class SimpleConvNetBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel):
nn.Module.__init__(self)
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel, padding=1)
self.relu = nn.ReLU... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | cle-ros/RoutingNetworks | SimpleConvNetBlock | false | 15,040 | [
"Apache-2.0"
] | 63 | 0f1fe1221c67a224a02bca6247d3c4488ede0a04 | https://github.com/cle-ros/RoutingNetworks/tree/0f1fe1221c67a224a02bca6247d3c4488ede0a04 |
AutoEncoder | # 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... | harryye930/ML-Performance-Prediction | AutoEncoder | false | 3,580 | [
"MIT"
] | 0 | 82fac16da3c2dde6054cf5b579aa6864e9d37b30 | https://github.com/harryye930/ML-Performance-Prediction/tree/82fac16da3c2dde6054cf5b579aa6864e9d37b30 |
SingleStream | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.init as torch_init
def calculate_l1_norm(f):
f_norm = torch.norm(f, p=2, dim=-1, keepdim=True)
f = torch.div(f, f_norm)
return f
class SingleStream(nn.Module):
def __init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LeonHLJ/MMSD | SingleStream | false | 10,781 | [
"MIT"
] | 0 | e39838e4e38524a670c08cc696a65da8ae01f648 | https://github.com/LeonHLJ/MMSD/tree/e39838e4e38524a670c08cc696a65da8ae01f648 |
AdaptiveConcatPool3d | # 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... | i-pan/kaggle-melanoma | AdaptiveConcatPool3d | false | 15,565 | [
"MIT"
] | 68 | caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 | https://github.com/i-pan/kaggle-melanoma/tree/caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 |
EdgeCaseModel | import torch
import torch.nn as nn
class LayerWithRidiculouslyLongNameAndDoesntDoAnything(nn.Module):
def forward(self, x):
return x
class EdgeCaseModel(nn.Module):
def __init__(self, throw_error=False, return_str=False):
super().__init__()
self.throw_error = throw_error
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EarthLab-Luxembourg/torch-summary | EdgeCaseModel | false | 412 | [
"MIT"
] | 0 | 8ef25aea5e9fb075df27e1e0c77bad56a7254397 | https://github.com/EarthLab-Luxembourg/torch-summary/tree/8ef25aea5e9fb075df27e1e0c77bad56a7254397 |
CoralLayer | import torch
class CoralLayer(torch.nn.Module):
""" Implements CORAL layer described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_cu... | Raschka-research-group/coral-pytorch | CoralLayer | false | 8,678 | [
"MIT"
] | 32 | 6b85e287118476095bac85d6f3dabc6ffb89a326 | https://github.com/Raschka-research-group/coral-pytorch/tree/6b85e287118476095bac85d6f3dabc6ffb89a326 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, inputLayer):
super(Net, self).__init__()
self.fc1 = nn.Linear(inputLayer, 100)
self.fc2 = nn.Linear(100, 2)
def forward(self, x):
x = self.fc1(x)
x = F.tanh(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Marissa4/RPyCA | Net | false | 11,688 | [
"MIT"
] | 0 | e3c229361a4cd9ddd53accc5541b7c8b5f8939e0 | https://github.com/Marissa4/RPyCA/tree/e3c229361a4cd9ddd53accc5541b7c8b5f8939e0 |
Lookahead | # 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... | maxwellzh/CAT | Lookahead | false | 16,051 | [
"Apache-2.0"
] | 237 | b1a9c3f95e84d968593a05bf8b176b5f77b8055e | https://github.com/maxwellzh/CAT/tree/b1a9c3f95e84d968593a05bf8b176b5f77b8055e |
FeatureAssembler | # 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 typing import Optional
import torch.nn as nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RSNirwan/gluon-ts | FeatureAssembler | false | 5,737 | [
"Apache-2.0"
] | 1 | ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 | https://github.com/RSNirwan/gluon-ts/tree/ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""A Layer Normalization layer.
Lei Ba, Jimmy, Jamie Ryan Kiros, and Geoffrey E. Hinton.
arXiv preprint arXiv:1607.06450 (2016).
"""
def __init__(self, dim):
super(LayerNorm, self).__init__()
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.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.... | awesome-archive/nmtpytorch | LayerNorm | false | 6,293 | [
"MIT"
] | 1 | 7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 | https://github.com/awesome-archive/nmtpytorch/tree/7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 |
ScaledDotProductAttention | # 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.... | AppleHolic/FastSpeech2 | ScaledDotProductAttention | false | 16,946 | [
"MIT"
] | 8 | 8f6969edd0c86c05b1dd70a0b7841bd86505455e | https://github.com/AppleHolic/FastSpeech2/tree/8f6969edd0c86c05b1dd70a0b7841bd86505455e |
SimpleMaxModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleMaxModule | false | 12,578 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
MyLayerNorm | import torch
import torch.nn as nn
class MyLayerNorm(nn.Module):
def __init__(self, input_dim):
super(MyLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(input_dim))
if True or use_bias:
self.beta = nn.Parameter(torch.ones(input_dim))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Ar-Kareem/Sketch-RNN | MyLayerNorm | false | 4,876 | [
"MIT"
] | 1 | 350824040715ea281182de01bca467130f326566 | https://github.com/Ar-Kareem/Sketch-RNN/tree/350824040715ea281182de01bca467130f326566 |
SingleStream | # 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.... | LeonHLJ/MMSD | SingleStream | false | 10,781 | [
"MIT"
] | 0 | e39838e4e38524a670c08cc696a65da8ae01f648 | https://github.com/LeonHLJ/MMSD/tree/e39838e4e38524a670c08cc696a65da8ae01f648 |
TotalVariations | import torch
from torch.nn.modules.loss import _Loss
class TotalVariations(_Loss):
def forward(self, img1):
return torch.sum(torch.abs(img1[:, :, :-1] - img1[:, :, 1:])
) + torch.sum(torch.abs(img1[:, :-1, :] - img1[:, 1:, :]))
def get_inputs():
return [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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | TotalVariations | false | 17,462 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 |
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.... | InfluencerNGZK/wenet | RelPositionMultiHeadedAttention | false | 2,405 | [
"Apache-2.0"
] | 0 | 9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e | https://github.com/InfluencerNGZK/wenet/tree/9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e |
DownConvBlock | # 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.... | jabae/detectEM | DownConvBlock | false | 6,925 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
VAE | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch.utils.data
class VAE(nn.Module):
def __init__(self, dim, middle=400, bottleneck=100):
super(VAE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, middle)
self.fc21 = nn.Linear(middle, bot... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | anandijain/audio | VAE | false | 6,203 | [
"MIT"
] | 1 | 1990de57ebc760cf6c5cc7132119b389cfd2dbfb | https://github.com/anandijain/audio/tree/1990de57ebc760cf6c5cc7132119b389cfd2dbfb |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChenRocks/Distill-BERT-Textgen-ONMT | PositionwiseFeedForward | false | 17,101 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
PairwiseBCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from abc im... | bogdankostic/flair | PairwiseBCELoss | false | 6,339 | [
"MIT"
] | 1 | 8cf03eab19512e94c1bcb4a30409bb065d37fe25 | https://github.com/bogdankostic/flair/tree/8cf03eab19512e94c1bcb4a30409bb065d37fe25 |
AE_2D_v2 | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D_v2(nn.Module):
def __init__(self, n_features=4):
super(AE_2D_v2, self).__init__()
self.en1 = nn.Linear(n_features, 50)
self.en2 = nn.Linear(50, 20)
self.en3 = nn.Linear(20, 10)
self.en4 = nn.Linear(10, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | gitter-badger/HEPAutoencoders | AE_2D_v2 | false | 12,436 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ankitkv/pylego | Classifier | false | 18,349 | [
"MIT"
] | 4 | 38d4a8fe8497d748b22c58313cbfd187efb8326e | https://github.com/ankitkv/pylego/tree/38d4a8fe8497d748b22c58313cbfd187efb8326e |
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.... | HKUST-KnowComp/BMGF-RoBERTa | MultiHeadAttn | false | 8,243 | [
"MIT"
] | 16 | 8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd | https://github.com/HKUST-KnowComp/BMGF-RoBERTa/tree/8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd |
BicubicUpsampler | # 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 as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | BicubicUpsampler | false | 8,307 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
SimpleGCN | # 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
from torch.nn.parameter import Parameter
from ... | akashgokul/kaolin | SimpleGCN | false | 3,056 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 6360c4f2bcdd81f461dfb4d96267e79d89d5e112 | https://github.com/akashgokul/kaolin/tree/6360c4f2bcdd81f461dfb4d96267e79d89d5e112 |
MFB | import torch
import torch.nn as nn
import torch.nn.functional as F
class MFB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_norm=0.0, dropout_output=0.0):
super(MFB, 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
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MFB | false | 11,561 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
SimpleResidualBlock | import torch
import torch.nn as nn
class SimpleResidualBlock(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3,
stride=1, padding=1)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(in_channels=3, out_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
import torch.nn as nn
assert_... | ayanch07/ResNet-cifar-10-pytorch | SimpleResidualBlock | false | 3,154 | [
"MIT"
] | 0 | bafc945a022a2e3ada689a831c7e57b5bdb0e8bd | https://github.com/ayanch07/ResNet-cifar-10-pytorch/tree/bafc945a022a2e3ada689a831c7e57b5bdb0e8bd |
MultiHead | import math
import torch
from torch import nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, d_key, drop_ratio, causal):
super(Attention, self).__init__()
self.scale = math.sqrt(d_key)
self.dropout = nn.Dropout(drop_ratio)
self.causal = causal
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Sy-Zhang/recurrent-transformer | MultiHead | false | 9,735 | [
"MIT"
] | 0 | f66ba49a2c9ec42759d3d00d497b49ffe39e18de | https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de |
GraphLevelAttentionLayer | # 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.... | Yonggie/HsCTRD | GraphLevelAttentionLayer | false | 2,981 | [
"Apache-2.0"
] | 0 | d4541f13630a6abd0e17b116ad6aeeab74f54f1c | https://github.com/Yonggie/HsCTRD/tree/d4541f13630a6abd0e17b116ad6aeeab74f54f1c |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | ishine/GANsNRoses | ModulatedConv2d | false | 15,624 | [
"MIT"
] | 969 | 414e9e77c3df47d4ecf7941b5dcfdffec67403ee | https://github.com/ishine/GANsNRoses/tree/414e9e77c3df47d4ecf7941b5dcfdffec67403ee |
Split | import torch
import torch.nn as nn
import torch.utils.data
class Split(nn.Module):
def __init__(self):
super(Split, self).__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XinZhang525/fGAIL | Split | false | 18,106 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b |
MLPAutoencoder | import torch
def choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | tailintalent/hamiltonian-nn | MLPAutoencoder | false | 16,541 | [
"Apache-2.0"
] | 293 | 1f6dd2d58ab84977a30584f0d1dd7f8b234e4049 | https://github.com/tailintalent/hamiltonian-nn/tree/1f6dd2d58ab84977a30584f0d1dd7f8b234e4049 |
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
import torch.nn as nn
assert_... | BHD233/PaddleOCR2Pytorch | FFN | false | 13,359 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
QNetworkMedium | import torch
import torch.nn as nn
import torch.nn.functional as F
class QNetworkMedium(nn.Module):
def __init__(self, state_size, action_size, seed):
"""
Build a fully connected neural network
state_size (int): State dimension
action_size (int): Action dimension
seed (in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Yigit-Arisoy/deep-rts | QNetworkMedium | false | 14,696 | [
"MIT"
] | 144 | a5ed2c29b76789830df9f7075480c7229ccf0f4d | https://github.com/Yigit-Arisoy/deep-rts/tree/a5ed2c29b76789830df9f7075480c7229ccf0f4d |
ConvNCFBPRLoss | # 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
... | Ahren09/RecBole | ConvNCFBPRLoss | false | 1,944 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
Abs | # 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | CoraJung/end-to-end-spoken-language-understanding | Abs | false | 5,018 | [
"Apache-2.0"
] | 1 | d1b15dad1a8f01336bcb0adcbf95d8c6ea279d09 | https://github.com/CoraJung/end-to-end-spoken-language-understanding/tree/d1b15dad1a8f01336bcb0adcbf95d8c6ea279d09 |
Foo | # 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.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | Foo | false | 5,822 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
GeneralizedDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import collections
from typi... | Irme/MONAI | GeneralizedDiceLoss | false | 17,472 | [
"Apache-2.0"
] | 3 | dc4bf661831b14f4231cb325cc1b15d38c1e406c | https://github.com/Irme/MONAI/tree/dc4bf661831b14f4231cb325cc1b15d38c1e406c |
RNN | # 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.autograd import Variable
assert_size_stride = t... | ChronosMasterOfAllTime/practical-pytorch | RNN | false | 11,313 | [
"MIT"
] | 0 | ed9567cec05ac348063c11963b6d05065fec3578 | https://github.com/ChronosMasterOfAllTime/practical-pytorch/tree/ed9567cec05ac348063c11963b6d05065fec3578 |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(sel... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AnonSubmission6150/submission6150 | PPMConcat | false | 8,998 | [
"Apache-2.0"
] | 0 | 571633d9a12b4fd7a9546947787fc068966dab04 | https://github.com/AnonSubmission6150/submission6150/tree/571633d9a12b4fd7a9546947787fc068966dab04 |
TransNonlinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class TransNonlinear(nn.Module):
def __init__(self, d_model, dim_feedforward, dropout=0.1):
super().__init__()
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jasonkks/PTTR | TransNonlinear | false | 8,345 | [
"Apache-2.0"
] | 14 | 11f664a7f1b2281293d82a5450fdd3d4bfa5883e | https://github.com/Jasonkks/PTTR/tree/11f664a7f1b2281293d82a5450fdd3d4bfa5883e |
ClusterAssignment | # 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
from torch.nn import Parameter
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | giorgosVardakas/pt-dec | ClusterAssignment | false | 15,435 | [
"MIT"
] | 200 | c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 | https://github.com/giorgosVardakas/pt-dec/tree/c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 |
PositionwiseFeedForward | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BoonthichaSaejia/ThaiSum | PositionwiseFeedForward | false | 7,815 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
CDFLayer | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class CDFLayer(nn.Module):
def __init__(self, device='cpu'):
super(CDFLayer, self).__init__()
self.loc_scale = Parameter(torch.FloatTensor([0.0, 1.0]))
def forward(self, x, dim=1):
m = torch.distributions.Cau... | 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 torch.nn.parameter import Parameter
assert_size_stri... | asifr/armisc | CDFLayer | false | 3,250 | [
"MIT"
] | 0 | 486220ba498353faeb94f70cd8ffe917109526d2 | https://github.com/asifr/armisc/tree/486220ba498353faeb94f70cd8ffe917109526d2 |
RZTXDecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class RZTXDecoderLayer(nn.Module):
"""RZTXDecoderLayer is made up of self-attn and feedforward network with
residual weights for faster convergece.
This encoder layer is based on the paper "ReZero is All You Need:
Fast Convergence at 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 import triton_helpers
from torch._inductor.runtime.... | mpariente/rezero | RZTXDecoderLayer | false | 16,125 | [
"MIT"
] | 376 | 6bcf1df00bc9a3560b093a2bbe12dade92f86eba | https://github.com/mpariente/rezero/tree/6bcf1df00bc9a3560b093a2bbe12dade92f86eba |
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