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 |
|---|---|---|---|---|---|---|---|---|---|---|
LabelPropagation | # 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_... | EdisonLeeeee/GraphGallery | LabelPropagation | false | 13,658 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
Backbone | import torch
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | dmoebius-dm/prototorch_models | Backbone | false | 3,435 | [
"MIT"
] | 0 | 71602bf38a09148eab13d98c9f89589b345ac570 | https://github.com/dmoebius-dm/prototorch_models/tree/71602bf38a09148eab13d98c9f89589b345ac570 |
PositionalEncoding | import math
import torch
class PositionalEncoding(torch.nn.Module):
"""
Positional encoding for Transformer
Parameters
----------
hidden_size : `int`, required
Hidden size of positional encoding.
Must match hidden size of input tokens.
dropout : `float`, required
Dropo... | 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda... | Nemexur/nonauto-lm | PositionalEncoding | false | 17,750 | [
"Apache-2.0"
] | 3 | 6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 | https://github.com/Nemexur/nonauto-lm/tree/6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 |
ExtendedModel | # 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_... | sauyon/BentoML | ExtendedModel | false | 4,282 | [
"Apache-2.0"
] | 0 | ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 | https://github.com/sauyon/BentoML/tree/ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 |
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.... | PaulPan00/donkey_wrapper | Actor | false | 17,831 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | salem-devloper/COVID-Lung-Segment | DiceLoss | false | 4,243 | [
"MIT"
] | 0 | 6896f6b0c56dac6d32e005afd4a94d59b1917b44 | https://github.com/salem-devloper/COVID-Lung-Segment/tree/6896f6b0c56dac6d32e005afd4a94d59b1917b44 |
ShiftedConv | # 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 numpy import prod
assert_size_stride = to... | leo19941227/CPC_audio | ShiftedConv | false | 12,704 | [
"MIT"
] | 0 | 2d0051915f4b4a5f773e4510cd5535e1fcb433d8 | https://github.com/leo19941227/CPC_audio/tree/2d0051915f4b4a5f773e4510cd5535e1fcb433d8 |
ResidualBlockNoBN | import torch
from torch import nn
class ResidualBlockNoBN(nn.Module):
"""
ResNet without Batch Normalisation
"""
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlockNoBN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_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
from torch import nn
assert_s... | LasseWolter/laughter-detection | ResidualBlockNoBN | false | 9,266 | [
"MIT"
] | 0 | f0a37f8e991fc57e8bbc846695fc4dea84d60af5 | https://github.com/LasseWolter/laughter-detection/tree/f0a37f8e991fc57e8bbc846695fc4dea84d60af5 |
FFN | import torch
import torch.nn as nn
from collections import OrderedDict
class FFN(nn.Module):
def __init__(self, layer_arch, input_size, output_size, bias=True):
super(FFN, self).__init__()
self.layer_arch = layer_arch
self.input_size = input_size
self.output_size = output_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | charlesxin97/ToolFinder_binder | FFN | false | 1,663 | [
"Apache-2.0"
] | 0 | 49467d5519adcd6d881e57d460c97c37b6a45add | https://github.com/charlesxin97/ToolFinder_binder/tree/49467d5519adcd6d881e57d460c97c37b6a45add |
AttnBertPooler | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class AttnBertPooler(nn.Module):
def __init__(self, config):
super(AttnBertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size * 2)
self.activation = nn.Tanh(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | AnonymousAuthor2013/PostRec | AttnBertPooler | false | 1,845 | [
"MIT"
] | 0 | a1461f716d177e28b96ca29d1398f96b5717c1e1 | https://github.com/AnonymousAuthor2013/PostRec/tree/a1461f716d177e28b96ca29d1398f96b5717c1e1 |
AdjDecoder | # 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... | BigkoalaZhu/SCORES | AdjDecoder | false | 7,784 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
Relation | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn import functional as F
class Relation(nn.Module):
def __init__(self, C, H, out_size):
super(Relation, self).__init__()
self.out_size = out_size
self.M = torch.nn.Parameter(torch.randn(H, H, out_size))
self.W ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ChenZhannnnn/chenzhan | Relation | false | 13,481 | [
"Apache-2.0"
] | 45 | b26a9512bbd1efe86c35c91a625da40b6f94dfc7 | https://github.com/ChenZhannnnn/chenzhan/tree/b26a9512bbd1efe86c35c91a625da40b6f94dfc7 |
ActorCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | GuanShiTing/DL_RL_Zoo | ActorCritic | false | 5,308 | [
"Apache-2.0"
] | 1 | 520cd92c1a28f64006d51444a0940cc645b95c6d | https://github.com/GuanShiTing/DL_RL_Zoo/tree/520cd92c1a28f64006d51444a0940cc645b95c6d |
AvgConsensus | # 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... | Alexis-Fab/mmaction2 | AvgConsensus | false | 11,202 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
Quantizer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Siraj-Qazi/BNN-PYNQ | Quantizer | false | 2,837 | [
"BSD-3-Clause"
] | 0 | b942fe92b3c62b0b877b0a9d5c13e7eb3a234685 | https://github.com/Siraj-Qazi/BNN-PYNQ/tree/b942fe92b3c62b0b877b0a9d5c13e7eb3a234685 |
Pool | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fx
class Pool(nn.Module):
def __init__(self):
super(Pool, self).__init__()
def forward(self, x):
return F.adaptive_avg_pool2d(x, (5, 5))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | NVIDIA/Torch-TensorRT | Pool | false | 14,078 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 |
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.... | CS-savvy/Transformer-for-Parkinsons-disease | MultiHeadAttention | false | 2,091 | [
"MIT"
] | 0 | 42ef54071092f4aab74c8b9ec82c52e944806a5b | https://github.com/CS-savvy/Transformer-for-Parkinsons-disease/tree/42ef54071092f4aab74c8b9ec82c52e944806a5b |
Entropy_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 math as tl_math
import torch.nn as nn
... | DAIZHENWEI/FastGCN_pytorch | Entropy_loss | false | 342 | [
"MIT"
] | 0 | 87efe350d5acbe517a0642e9862ac9676b55c053 | https://github.com/DAIZHENWEI/FastGCN_pytorch/tree/87efe350d5acbe517a0642e9862ac9676b55c053 |
ClassPredictor | import torch
from torch import nn
class ClassPredictor(nn.Module):
def __init__(self, nz_feat, max_object_classes):
super(ClassPredictor, self).__init__()
self.predictor = nn.Linear(nz_feat, max_object_classes)
def forward(self, feats):
class_logits = self.predictor(feats)
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JasonQSY/Associative3D | ClassPredictor | false | 8,337 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
SinActv | import torch
import torch.nn as nn
class SinActv(nn.Module):
"""The sin activation function.
"""
def __init__(self):
"""Initializer method.
"""
super().__init__()
def forward(self, input_):
return torch.sin(input_)
def get_inputs():
return [torch.rand([4, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | aksj98/neurodiffeq | SinActv | false | 1,392 | [
"MIT"
] | 0 | f0ee8ee1e78f2aeee97a4e1fc887ebe0b50749c8 | https://github.com/aksj98/neurodiffeq/tree/f0ee8ee1e78f2aeee97a4e1fc887ebe0b50749c8 |
Attention | import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.init
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
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.... | KunpengLi1994/VSRN | Attention | false | 13,995 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
ComplexConv2d | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ComplexConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, **kwargs):
super... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.onnx.operators
import... | IIP-Sogang/Audio-Visual-Speech-Recognition | ComplexConv2d | false | 17,452 | [
"MIT"
] | 9 | bd03be91135acbc6162b83092d462b7fe71dd007 | https://github.com/IIP-Sogang/Audio-Visual-Speech-Recognition/tree/bd03be91135acbc6162b83092d462b7fe71dd007 |
DenseSAGEConv | # 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.... | GrumpyZhou/pytorch_geometric | DenseSAGEConv | false | 5,255 | [
"MIT"
] | 1 | 88c54e72d3e26ad48e9ccd99e5696c7f19269d94 | https://github.com/GrumpyZhou/pytorch_geometric/tree/88c54e72d3e26ad48e9ccd99e5696c7f19269d94 |
BertSelfOutput | # 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.utils.... | adymaharana/VLCStoryGan | BertSelfOutput | false | 18,269 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
TimeEncode | # 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 ... | IDSC-io/vre-tgn | TimeEncode | false | 9,143 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 |
Q | # 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... | AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem | Q | false | 8,928 | [
"MIT"
] | 0 | 9d837b8df9c761defb1eca390b3a60aa4a6fbb1a | https://github.com/AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem/tree/9d837b8df9c761defb1eca390b3a60aa4a6fbb1a |
SimpleTanhModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | opti-mix/glow | SimpleTanhModel | false | 7,420 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
WeightedCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Chuxwa/OpenPCDet | WeightedCrossEntropyLoss | false | 5,010 | [
"Apache-2.0"
] | 1 | be064eafee68cb23f4bbe7decf2286ef13a94ebb | https://github.com/Chuxwa/OpenPCDet/tree/be064eafee68cb23f4bbe7decf2286ef13a94ebb |
FC_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class FC_Q(nn.Module):
def __init__(self, state_dim, num_actions):
super(FC_Q, self).__init__()
self.q1 = nn.Linear(state_dim, 256)
self.q2 = nn.Linear(256, 256)
self.q3 = nn.Linear(256, num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Altriaex/d4rl_evaluations | FC_Q | false | 8,974 | [
"Apache-2.0"
] | 0 | ceb34c04e98af9332c6338a1414c0c2aa5fea68b | https://github.com/Altriaex/d4rl_evaluations/tree/ceb34c04e98af9332c6338a1414c0c2aa5fea68b |
RRDB | import torch
import torch.nn as nn
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Geeta-Landmark/Super-Resolution-Image | RRDB | false | 11,472 | [
"Apache-2.0"
] | 0 | fb5d71ec9a4673409ecd28189e97056943ca308b | https://github.com/Geeta-Landmark/Super-Resolution-Image/tree/fb5d71ec9a4673409ecd28189e97056943ca308b |
Fp32LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | CUMLSec/stateformer | Fp32LayerNorm | false | 7,899 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
GeLU | import torch
from torch import nn
import torch.jit
import torch.nn.functional
import torch.nn
from torch.nn.functional import gelu
class GeLU(nn.Module):
def forward(self, x):
return gelu(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.jit
import torch.nn.functional
import torch.n... | Gitsamshi/nnUNet-1 | GeLU | false | 8,150 | [
"Apache-2.0"
] | 28 | 5341684211e6d91dab6ad76a7595a95addff23be | https://github.com/Gitsamshi/nnUNet-1/tree/5341684211e6d91dab6ad76a7595a95addff23be |
period_L2 | import torch
import numpy as np
import torch.nn as nn
def reduction_mean(loss):
return loss.mean()
def reduction_none(loss):
return loss
def reduction_sum(loss):
return loss.sum()
class period_L2(nn.Module):
def __init__(self, reduction='sum'):
"""
periodic Squared Error
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | flytocc/RAPiD | period_L2 | false | 15,358 | [
"MIT"
] | 142 | 92e6a44b8a0107def055e93c971d78fd548562f8 | https://github.com/flytocc/RAPiD/tree/92e6a44b8a0107def055e93c971d78fd548562f8 |
MaskedLinearSeqDup | import torch
import torch.cuda
from torch.nn.functional import *
class MaskedLinear(torch.nn.Linear):
def forward(self, x, mask):
out = super().forward(x)
if mask.is_floating_point():
out = out * mask
else:
out = out * mask.type_as(out)
return out
class M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
from torch.nn.functional import *
assert_size_stride = torch._... | bratao/DeepSpeed | MaskedLinearSeqDup | false | 6,361 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
Conv2dLayer | 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LenKerr/Semantic-Colorization-GAN | Conv2dLayer | false | 5,513 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
FocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
Implementation of the binary focal loss proposed in:
https://arxiv.org/abs/1708.02002
"""
def __init__(self, alpha: 'float'=1.0, gamma: 'float'=2.0, reduce:
'str'='mean') ->None:
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | ChristophReich1996/3D_Baggage_Segmentation | FocalLoss | false | 5,002 | [
"MIT"
] | 1 | 00392cb0fde22d3180b6baf81e404d0fcf4e2ebf | https://github.com/ChristophReich1996/3D_Baggage_Segmentation/tree/00392cb0fde22d3180b6baf81e404d0fcf4e2ebf |
BothContextGate | # 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 ... | AndrewM1998/MultimodalNMT | BothContextGate | false | 7,717 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
VectorQuantizer | # 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... | mateoIdemia/PyTorch-VAE | VectorQuantizer | false | 12,766 | [
"Apache-2.0"
] | 0 | b485924182e62843aae1955fcaf0886ac8492295 | https://github.com/mateoIdemia/PyTorch-VAE/tree/b485924182e62843aae1955fcaf0886ac8492295 |
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.... | ChoiIseungil/vilbert-multi-task | Attention | false | 5,001 | [
"MIT"
] | 1 | 37d14b9aed9c48117a820e05157c7ccd3dd20d5b | https://github.com/ChoiIseungil/vilbert-multi-task/tree/37d14b9aed9c48117a820e05157c7ccd3dd20d5b |
Myloss | import torch
import torch.nn as nn
class Myloss(nn.Module):
def __init__(self, epsilon=1e-08):
super(Myloss, self).__init__()
self.epsilon = epsilon
return
def forward(self, input_, label, weight):
entropy = -label * torch.log(input_ + self.epsilon) - (1 - label
)... | 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
... | MoriZSJ/GVB | Myloss | false | 2,656 | [
"MIT"
] | 0 | 9b954660ef377ead81c8e631c4a0f4a17075b2ea | https://github.com/MoriZSJ/GVB/tree/9b954660ef377ead81c8e631c4a0f4a17075b2ea |
SELECT_fusion_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
import torch.nn.parallel
impo... | RongchangLi/DEN | SELECT_fusion_block | false | 17,883 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
BasicConvBlock | import torch
from torch import nn
import torch.nn.functional as F
class BasicConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(BasicConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
padding=1)
self.conv2 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | royerloic/aydin | BasicConvBlock | false | 16,345 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
GramMatrix | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jay2020-01/TextureGAN--Flask | GramMatrix | false | 17,471 | [
"MIT"
] | 5 | cddea505b0d66b58d58fb24435f8bae42fd5a852 | https://github.com/Jay2020-01/TextureGAN--Flask/tree/cddea505b0d66b58d58fb24435f8bae42fd5a852 |
HardMish | # 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... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | HardMish | false | 15,074 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
GradLoss | import torch
import torch.nn as nn
class GradLoss(nn.Module):
def __init__(self):
super(GradLoss, self).__init__()
def forward(self, grad_fake, grad_real):
return torch.sum(torch.mean(torch.abs(grad_real - grad_fake)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Khoronus/MonoDepth-FPN-PyTorch | GradLoss | false | 721 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
AE_3D_small_v2 | # 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 ... | gitter-badger/HEPAutoencoders | AE_3D_small_v2 | false | 12,433 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Attention | import torch
import torch.nn.functional as F
from torch import nn
class Attention(nn.Module):
"""Attention Layer merging Encoder and Decoder
Attributes:
hidden_size (int):
The number of features in the hidden state h
Reference:
* https://github.com/bentrevett/pytorch-seq2seq
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | appleparan/mise.py | Attention | false | 6,233 | [
"MIT"
] | 1 | a77ea51be37a739928600c66d168d69b78bc0c4b | https://github.com/appleparan/mise.py/tree/a77ea51be37a739928600c66d168d69b78bc0c4b |
QConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import Tensor
import torch.nn as nn
import torch.autograd as A
from t... | i207M/pytorch-cifar | QConv2d | false | 10,229 | [
"MIT"
] | 0 | df4417b6d0a25515ac82b5aa6151ae2135b2cd5c | https://github.com/i207M/pytorch-cifar/tree/df4417b6d0a25515ac82b5aa6151ae2135b2cd5c |
ConvBlockFixup | # 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... | OmarNajdi/dl-for-har | ConvBlockFixup | false | 934 | [
"MIT"
] | 0 | 5e1b7c29caf2b41fcba106cd901c45d8f2d18429 | https://github.com/OmarNajdi/dl-for-har/tree/5e1b7c29caf2b41fcba106cd901c45d8f2d18429 |
Encoder | import torch
from torch import nn
import torch.hub
import torch.nn.functional as F
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N, audio_channels):
super(Encoder, self).__init__()
self.L, self.N = L, N
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... | JavierCane/demucs | Encoder | false | 5,392 | [
"MIT"
] | 1 | 01d14844a71be7b5d86adf06a8501a951157c3fe | https://github.com/JavierCane/demucs/tree/01d14844a71be7b5d86adf06a8501a951157c3fe |
GatedConv2d | # 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... | AWehenkel/UMNN | GatedConv2d | false | 13,259 | [
"BSD-3-Clause"
] | 69 | f93cb36040783dd60e14e0eda927899d3919825c | https://github.com/AWehenkel/UMNN/tree/f93cb36040783dd60e14e0eda927899d3919825c |
_FakeMegatronMLP | # 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 ... | liuhatry/fastmoe | _FakeMegatronMLP | false | 3,989 | [
"Apache-2.0"
] | 0 | a676bf1eae874c208a0e669bf0f79e6fb3b43623 | https://github.com/liuhatry/fastmoe/tree/a676bf1eae874c208a0e669bf0f79e6fb3b43623 |
SPoC_pooling | # 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... | SIJIEJI/2020-ai-meets-beauty_ntubeauty | SPoC_pooling | false | 5,793 | [
"MIT"
] | 1 | fede564fb3e3029f3fadfe107484c5c7e39c29c5 | https://github.com/SIJIEJI/2020-ai-meets-beauty_ntubeauty/tree/fede564fb3e3029f3fadfe107484c5c7e39c29c5 |
SIMSE | import torch
import torch.nn as nn
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
return simse
def get_input... | 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... | Columbine21/TFR-Net | SIMSE | false | 17,126 | [
"MIT"
] | 7 | 1da01577542e7f477fdf7323ec0696aebc632357 | https://github.com/Columbine21/TFR-Net/tree/1da01577542e7f477fdf7323ec0696aebc632357 |
Bottleneck | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.norm1 = nn.GroupNor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | andyqmongo/InstAParam | Bottleneck | false | 18,336 | [
"MIT"
] | 3 | 00494d5367ec32b4ce90d01778cba9d4f1166833 | https://github.com/andyqmongo/InstAParam/tree/00494d5367ec32b4ce90d01778cba9d4f1166833 |
CEL | # 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... | Farzanehkaji/https-github.com-lartpang-MINet | CEL | false | 11,414 | [
"MIT"
] | 0 | db7f5e64be4d28df2bfc68409b56c3f97d6388f1 | https://github.com/Farzanehkaji/https-github.com-lartpang-MINet/tree/db7f5e64be4d28df2bfc68409b56c3f97d6388f1 |
SReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | bpicnbnk/ISONet | SReLU | false | 1,572 | [
"MIT"
] | 0 | 993150fa215dbc92460142ade59a3a5ec4a0efd9 | https://github.com/bpicnbnk/ISONet/tree/993150fa215dbc92460142ade59a3a5ec4a0efd9 |
MultiHead | # 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.... | CFF-Dream/pytorch_geometric | MultiHead | false | 2,045 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sharingsky/resrep | SEBlock | false | 9,520 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
ResNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNet(nn.Module):
"""Modified ResNet model class"""
def __init__(self, block, num_blocks, depth, width=1):
super(ResNet, self).__init__()
self.iters = int((depth - 4) // 4)
self.in_planes = int(width * 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maosef/easy-to-hard | ResNet | false | 8,555 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
AdversarialNetwork | import torch
import torch.utils.data
import torch
import torch.nn as nn
class AdversarialNetwork(nn.Module):
def __init__(self, in_feature):
super(AdversarialNetwork, self).__init__()
self.ad_layer1 = nn.Linear(in_feature, 1024)
self.ad_layer2 = nn.Linear(1024, 1024)
self.ad_layer... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | FigaroK/pytorch-CycleGAN-and-pix2pix | AdversarialNetwork | false | 9,072 | [
"BSD-3-Clause"
] | 0 | 74407363baf4626782398040e34a342e20915d41 | https://github.com/FigaroK/pytorch-CycleGAN-and-pix2pix/tree/74407363baf4626782398040e34a342e20915d41 |
ConcatELU | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConcatELU(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | GBATZOLIS/CAFLOW | ConcatELU | false | 17,278 | [
"MIT"
] | 6 | ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 | https://github.com/GBATZOLIS/CAFLOW/tree/ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 |
LayerNorm | import torch
import torch.autograd
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
epsilon=None, conditional=False, hidden_units=None,
hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
super(LayerNorm, ... | 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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dyna... | dumpmemory/W2NER | LayerNorm | false | 15,271 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda |
LayerNorm | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
epsilon=None, conditional=False, hidden_units=None,
hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
... | 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.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | cwxcwx0319/Dictionary | LayerNorm | false | 15,097 | [
"Apache-2.0"
] | 82 | 55fb9a602a212f9c3a69a318fec31da1d07279df | https://github.com/cwxcwx0319/Dictionary/tree/55fb9a602a212f9c3a69a318fec31da1d07279df |
MultiHead | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MichiganCOG/Video-Grounding | MultiHead | false | 8,549 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
BCELoss2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | jayden-chua/image-mask | BCELoss2d | false | 3,689 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
LinearThreeDeep | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearThreeDeep(nn.Module):
def __init__(self, inputSize, hiddenSize1, hiddenSize2, hiddenSize3,
outputSize):
super().__init__()
self.inputLinear = nn.Linear(inputSize, hiddenSize1)
self.hiddenLinear1 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dmechea/PyTorch-CartPole | LinearThreeDeep | false | 1,853 | [
"MIT"
] | 0 | 9f49ac7b2ae59882e5ea66cc8f43f0354a120c49 | https://github.com/dmechea/PyTorch-CartPole/tree/9f49ac7b2ae59882e5ea66cc8f43f0354a120c49 |
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, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 32, 3)
self.pool2 = nn.MaxPool2d(3, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MonteYang/P1_Facial_Keypoints | Net | false | 11,740 | [
"MIT"
] | 0 | 1e3e4c9c6b48ec241f6fc7e072b25c7211cebd18 | https://github.com/MonteYang/P1_Facial_Keypoints/tree/1e3e4c9c6b48ec241f6fc7e072b25c7211cebd18 |
DynamicsModel | # 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.functional as F
assert_size_stride = torch... | si0wang/transfer_dmc | DynamicsModel | false | 12,985 | [
"MIT"
] | 0 | 6bda773244e0b709b3c13add2597f5f1cd01bfd7 | https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7 |
DiceLoss | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | 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 collections
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing impor... | Irme/MONAI | DiceLoss | false | 17,438 | [
"Apache-2.0"
] | 3 | dc4bf661831b14f4231cb325cc1b15d38c1e406c | https://github.com/Irme/MONAI/tree/dc4bf661831b14f4231cb325cc1b15d38c1e406c |
NormedLinear | import torch
import torch.nn.functional as F
import torch.nn as nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Bin-ze/Food_detection | NormedLinear | false | 17,004 | [
"Apache-2.0"
] | 4 | 1c1a067f12644f2b0289e49aec4637d580722f70 | https://github.com/Bin-ze/Food_detection/tree/1c1a067f12644f2b0289e49aec4637d580722f70 |
TorchModel | # 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_... | DLPerf/elasticdl | TorchModel | false | 8,937 | [
"MIT"
] | 0 | b9c03ea0e81861ae8d349c3d8ffd1f7b588b910b | https://github.com/DLPerf/elasticdl/tree/b9c03ea0e81861ae8d349c3d8ffd1f7b588b910b |
Softplus | import torch
import numpy as np
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
def activation_shifting(activation):
def shifted_activation(x):
return activation(x) - activation(torch.zeros_like(x))
return shifted_activation
def cauchy_softplus(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, math as tl_math
import numpy as np
from torch.utils.data import Dataset as Dat... | KelvinKan/CP-Flow | Softplus | false | 13,931 | [
"MIT"
] | 64 | d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 | https://github.com/KelvinKan/CP-Flow/tree/d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1 |
ClassificationNet | import torch
import torch.nn.functional as F
from torch import nn
class ClassificationNet(nn.Module):
def __init__(self, num_classes=10, num_digits=2):
super(ClassificationNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | YIFEI-MA/MultiDigitRecognition | ClassificationNet | false | 12,000 | [
"MIT"
] | 0 | f1f9567c31102ccdc7464a35b8a7c533b5d46734 | https://github.com/YIFEI-MA/MultiDigitRecognition/tree/f1f9567c31102ccdc7464a35b8a7c533b5d46734 |
SimpleNet | # 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 ... | aakgun/pytorch-VideoDataset | SimpleNet | false | 3,014 | [
"MIT"
] | 0 | 619e385f37b99bfabca0b814673825ed902242b2 | https://github.com/aakgun/pytorch-VideoDataset/tree/619e385f37b99bfabca0b814673825ed902242b2 |
SELU | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
def where(condition, if_true, if_false):
"""
Torch equivalent of numpy.where.
Parameters
----------
condition : torch.ByteTensor or torch.cuda.ByteTensor or torch.autograd.Variable
Condi... | 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.autograd import Variable
import torch.nn.funct... | dearkafka/inferno | SELU | false | 1,829 | [
"Apache-2.0"
] | 0 | e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | https://github.com/dearkafka/inferno/tree/e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | aivaslab/marlgrid | Net | false | 3,051 | [
"Apache-2.0"
] | 0 | 10b53d27ce224fadeeb5830d6034350a69feb4b4 | https://github.com/aivaslab/marlgrid/tree/10b53d27ce224fadeeb5830d6034350a69feb4b4 |
DeepQNetwork | # 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 as T
import torc... | SuperSaiyan-God/Reinforcement-Learning | DeepQNetwork | false | 4,435 | [
"MIT"
] | 0 | b43a2997e28ec3bf437c37d060637f6deecf89c6 | https://github.com/SuperSaiyan-God/Reinforcement-Learning/tree/b43a2997e28ec3bf437c37d060637f6deecf89c6 |
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._inductor.runtime.... | dqawami/openvino_training_extensions | Net | false | 15,223 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
_Classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class _Classifier(nn.Module):
def __init__(self, z_c_dim):
super(_Classifier, self).__init__()
self.fc1 = nn.Linear(z_c_dim, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, z_c):
h =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | mori97/revae | _Classifier | false | 4,031 | [
"MIT"
] | 0 | 465009076a9be78e8ddb9021a0699b32fc695f30 | https://github.com/mori97/revae/tree/465009076a9be78e8ddb9021a0699b32fc695f30 |
down_shifted_conv2d | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm as wn
def down_shift(x, pad=None):
xs = [int(y) for y in x.size()]
x = x[:, :, :xs[2] - 1, :]
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
class down_shifted_conv2d(nn.Module):
def __init__(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ajayjain/lmconv | down_shifted_conv2d | false | 14,782 | [
"MIT"
] | 69 | e00576de5118702c90493e88c6e459b0e45d1290 | https://github.com/ajayjain/lmconv/tree/e00576de5118702c90493e88c6e459b0e45d1290 |
LogisticRegression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jayelm/pytorch-project-template | LogisticRegression | false | 6,924 | [
"MIT"
] | 1 | 30306ce07b21c97c6993432764cbbe0a73092a0c | https://github.com/jayelm/pytorch-project-template/tree/30306ce07b21c97c6993432764cbbe0a73092a0c |
BasicNN | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class BasicNN(nn.Module):
def __init__(self):
super(BasicNN, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, x):
if type(x) == np.ndarray:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gtfierro/clipper | BasicNN | false | 10,159 | [
"Apache-2.0"
] | 0 | 88d7c238d51d5cf66d118bffca0c17edee84755e | https://github.com/gtfierro/clipper/tree/88d7c238d51d5cf66d118bffca0c17edee84755e |
InnerProductDecoder | import torch
import torch.fx
import torch.utils.data
class InnerProductDecoder(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | InnerProductDecoder | false | 504 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
FeedForwardLayer | import torch
from torch import nn
class FeedForwardLayer(nn.Module):
def __init__(self, hidden_size):
super(FeedForwardLayer, self).__init__()
self.linear_1 = nn.Linear(hidden_size, 4 * hidden_size)
self.linear_2 = nn.Linear(4 * hidden_size, hidden_size)
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
from torch import nn
assert_s... | yongho94/Transformers_NMT | FeedForwardLayer | false | 11,021 | [
"MIT"
] | 0 | 14fb08a6b1391da4d49f199dc16d7beb37620c98 | https://github.com/yongho94/Transformers_NMT/tree/14fb08a6b1391da4d49f199dc16d7beb37620c98 |
SimpleLogModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogModule(torch.nn.Module):
def __init__(self, *dimensions):
super(SimpleLogModule, self).__init__()
def forward(self, a):
b = torch.log(a)
return torch.log(b)
def get_inputs():
return [torch.rand([4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | opti-mix/glow | SimpleLogModule | false | 7,401 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
TorchEntityRecognizer | import torch
from typing import List
from collections import OrderedDict
from torch import nn
def is_dropout_module(module: 'nn.Module', dropout_modules:
'List[nn.Module]'=[nn.Dropout, nn.Dropout2d, nn.Dropout3d]) ->bool:
"""Detect if a PyTorch Module is a Dropout layer
module (nn.Module): Module to check... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | apjanco/projects | TorchEntityRecognizer | false | 14,891 | [
"MIT"
] | 823 | 2f8850140ba13ab18b9cf622e46e79013d41701f | https://github.com/apjanco/projects/tree/2f8850140ba13ab18b9cf622e46e79013d41701f |
Cosh | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mil-tokyo/webdnn | Cosh | false | 16,080 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
net2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class net2(nn.Module):
"""
"""
def __init__(self, n_classes=2):
super(net2, self).__init__()
if torch.cuda.is_available():
torch.device('cuda')
else:
torch.device('cpu')
self.n_class... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | luisesanmartin/dwelling-recognition | net2 | false | 7,139 | [
"MIT"
] | 1 | b2437b64088a26746947c1c88077c96332e7b9c6 | https://github.com/luisesanmartin/dwelling-recognition/tree/b2437b64088a26746947c1c88077c96332e7b9c6 |
Invertible1x1Conv | import torch
from torch.nn import functional as F
from torch.autograd import Variable
import torch.utils.data
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import functional as F
from torch.autograd import Variable
import ... | ishalyminov/shad_speech | Invertible1x1Conv | false | 15,611 | [
"MIT"
] | 83 | e1345d2de929e150b2683190b127a837fbcb34f3 | https://github.com/ishalyminov/shad_speech/tree/e1345d2de929e150b2683190b127a837fbcb34f3 |
ConvolutionModule | import torch
from torch import Tensor
from torch import nn
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: 'Tensor') ->Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
class ConvolutionModule(nn.Module):
"""ConvolutionModule... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 T... | desh2608/icefall | ConvolutionModule | false | 3,420 | [
"Apache-2.0"
] | 0 | 1603744469d167d848e074f2ea98c587153205fa | https://github.com/desh2608/icefall/tree/1603744469d167d848e074f2ea98c587153205fa |
injective_pad | # 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... | Schwartz-Zha/My-invertible-resnet | injective_pad | false | 1,034 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
ResBlock | import torch
class ResBlock(torch.nn.Module):
def __init__(self, num_channel):
super(ResBlock, self).__init__()
self.conv1 = torch.nn.Conv2d(num_channel, num_channel, kernel_size=
3, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(num_channel, num_channel, kernel_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Gregory-Eales/mban | ResBlock | false | 5,233 | [
"Apache-2.0"
] | 1 | d8b35db51c7e601b1db777d9a80343600374250b | https://github.com/Gregory-Eales/mban/tree/d8b35db51c7e601b1db777d9a80343600374250b |
Classify | # 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... | AkshayGanesh/yolov5processor | Classify | false | 4,813 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad |
Add_ParamI | # 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.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AlexMeinke/Provable-OOD-Detection | Add_ParamI | false | 7,684 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Multi_Head_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | tianjiansmile/Chinese-Text-Classification-Pytorch | Multi_Head_Attention | false | 10,887 | [
"MIT"
] | 0 | 05cc211b161f61e6bb32ab185dadcffec2f5b5de | https://github.com/tianjiansmile/Chinese-Text-Classification-Pytorch/tree/05cc211b161f61e6bb32ab185dadcffec2f5b5de |
StructuralProbe | import torch
import torch.nn as nn
import torch.utils.data.dataloader
class StructuralProbe(nn.Module):
""" Computes squared L2 distance after projection by a matrix.
For a batch of sentences, computes all n^2 pairs of distances
for each sentence in the batch.
"""
def __init__(self, model_dim, ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.dataloader
assert_size_stride = to... | TimO96/NLP2 | StructuralProbe | false | 1,140 | [
"MIT"
] | 0 | 83f65a385457f68397c641f38b53df0110282578 | https://github.com/TimO96/NLP2/tree/83f65a385457f68397c641f38b53df0110282578 |
PixLoss | import torch
import torch.nn as nn
class PixLoss(nn.Module):
"""Pixel-wise MSE loss for images"""
def __init__(self, alpha=20):
super().__init__()
self.alpha = alpha
def forward(self, fake, real):
return self.alpha * torch.mean((fake - real) ** 2)
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | akanametov/SuperResolution | PixLoss | false | 6,135 | [
"MIT"
] | 1 | 45313d1309ddb5cdef821aaf5ac7b5ad574b5287 | https://github.com/akanametov/SuperResolution/tree/45313d1309ddb5cdef821aaf5ac7b5ad574b5287 |
ZeroConv2d | # 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
from torch im... | HugoSenetaire/glow-pytorch | ZeroConv2d | false | 567 | [
"MIT"
] | 0 | 7f11be87cac9770df63867910c34738dedee6f56 | https://github.com/HugoSenetaire/glow-pytorch/tree/7f11be87cac9770df63867910c34738dedee6f56 |
ResidualNetworkSegment | # 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_... | Arjung27/DeepThinking | ResidualNetworkSegment | false | 16,967 | [
"MIT"
] | 6 | 13a2ce534bcb0b9379a22fffef52d975d650adb2 | https://github.com/Arjung27/DeepThinking/tree/13a2ce534bcb0b9379a22fffef52d975d650adb2 |
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